Large Language Models (LLMs) still struggle with multi-step logical reasoning.Existing approaches either purely refine the reasoning chain in natural language form or attach a symbolic solver as an external module.In this work, we instead ask whether LLMs contain a shared internal logical subspace that simultaneously aligns natural-language and symbolic-language views of the reasoning process.Our hypothesis is that this logical subspace captures logical reasoning capabilities in LLMs that are shared across views while remaining independent of surface forms.To verify this, we employ Canonical Correlation Analysis on the paired residual activations from natural-language and symbolic-language reasoning chains, learning a low-dimensional subspace with maximum cross-view correlation.Furthermore, we design a training-free approach that steers LLMs reasoning chain along this logical subspace, thereby leveraging the complementary reasoning signals from both views.Experiments on four logical reasoning benchmarks demonstrate the effectiveness of our approach, improving accuracy by up to 11 percentage points and generalizing well on out-of-domain problems 1 .

Abstract Stochastic bilevel optimization finds widespread applications in machine learning, including meta-learning, hyperparameter optimization, and neural architecture search. To extend stochastic bilevel optimization to distributed data, several decentralized stochastic bilevel optimization algorithms have been developed. However, existing methods often suffer from slow convergence rates and high communication costs in heterogeneous settings, limiting their applicability to real-world tasks. To address these issues, we propose two novel decentralized stochastic bilevel gradient descent algorithms based on simultaneous and alternating update strategies. Our algorithms can achieve faster convergence rates and lower communication costs than existing methods. Importantly, our convergence analyses do not rely on strong assumptions regarding heterogeneity. More importantly, our theoretical analyses clearly disclose how the computation and communication regarding the Hessian-inverse-vector product under the heterogeneous setting affects the convergence rate. To the best of our knowledge, this is the first time such favorable theoretical results have been achieved with mild assumptions in the heterogeneous setting. Furthermore, we demonstrate how to establish the convergence rate for the alternating update strategy when combined with the variance-reduced gradient. Finally, experimental results confirm the efficacy of our algorithms.

Combating misinformation in the digital age has been a challenging subject with significant social implications, as misinformation continues to shape public responses to elections, pandemics, and other critical events. Despite extensive research efforts, traditional mitigation approaches have fallen short, largely due to their narrow focus on detection accuracy while overlooking the social and emotional influences that drive user behavior. This article proposes to frame information belief as a social norm, enabling us to track and predict its evolution at the individual user level. By operating on an individual level, we can identify and target specific users susceptible to adopting certain norms ahead of time, providing a more powerful approach to mitigating the effects of misinformation. To accomplish this, we develop a temporal graph neural network equipped with projection operators capable of forecasting arbitrary time horizons. The network is trained to predict individual users’ tipping points—the moments when they transition from resistance to acceptance of particular information norms. We conduct rigorous analysis of the temporal smoothness of our predictions, leading to an algorithm that queries the model to identify critical moments before users reach their tipping points, enabling preemptive action. To our knowledge, this represents the first successful attempt at predicting individual tipping points in real-world information networks, marking a significant advance in our ability to address misinformation through preventive rather than reactive measures.

Federated Learning (FL) has amassed notable attention for its ability to preserve user privacy while emphasizing the retainment of model training efficiency. Due to this potential, FL has been integrated in many domains, such as healthcare, finance, law, and industrial engineering, where data cannot be easily exchanged due to sensitive information and strict privacy laws. However, current research has indicated that FL protocols are easily compromised by active data reconstruction attacks employed by actively dishonest servers. The malicious modification of global model parameters allows an actively dishonest server to obtain a direct copy of users’ private data via gradient inversion. This class of attacks is highly underexplored and continues to be a major challenge due to the intense threat model. In this article, we propose OASIS as a scalable and modality-agnostic defense based on data augmentation that counteracts active data reconstruction attacks while preserving model performance. To generalize our defense, we uncover the intuition behind gradient inversion that enables these attacks and theoretically establish the conditions by which the defense can be considered robust regardless of attack design. From this, we formulate our defense with data augmentation that illustrates its ability to undermine the attack principle. We evaluate OASIS on five real-world datasets–two image-based (ImageNet and CIFAR100) and three text-based (Wikitext, Stack Overflow, and Shakespeare)–which span diverse uses cases such as vision tasks and language modeling. Comprehensive evaluations on these datasets exhibit the efficacy of OASIS and highlight its feasibility as a solution.

Quantum annealing (QA) has great potential to solve combinatorial optimization problems efficiently. However, the effectiveness of QA algorithms is heavily based on the embedding of problem instances, represented as logical graphs, into the quantum processing unit (QPU) whose topology is in the form of a limited connectivity graph, known as the minor embedding problem. Because the minor embedding problem is an NP-hard problem [ 11 ], existing methods for the minor embedding problem suffer from scalability issues when faced with larger problem sizes. In this article, we propose a novel approach utilizing Reinforcement Learning (RL) techniques to address the minor embedding problem, named CHARME. CHARME includes three key components: a Graph Neural Network (GNN) architecture for policy modeling, a state transition algorithm that ensures solution validity, and an order exploration strategy for effective training. Through comprehensive experiments on synthetic and real-world instances, we demonstrate the efficiency of our proposed order exploration strategy as well as our proposed RL framework, CHARME. In particular, CHARME yields superior solutions in terms of qubit usage compared to fast embedding methods such as Minorminer and ATOM. Moreover, our method surpasses the OCT-based approach, known for its slower runtime but high-quality solutions, in several cases. In addition, our proposed exploration enhances the efficiency of the training of the CHARME framework by providing better solutions compared to the greedy strategy.

Quantum computing has the potential to revolutionize fields like quantum optimization and quantum machine learning. However, current quantum devices are hindered by noise, reducing their reliability. A key challenge in gate-based quantum computing is improving the reliability of quantum circuits, measured by process fidelity, during the transpilation process, particularly in the routing stage. In this article, we address the Fidelity Maximization in Routing Stage (FMRS) problem by introducing FIDDLE, a novel learning framework comprising two modules: a Gaussian Process-based surrogate model to estimate process fidelity with limited training samples and a reinforcement learning module to optimize routing. Our approach is the first to directly maximize process fidelity, outperforming traditional methods that rely on indirect metrics such as circuit depth or gate count. We rigorously evaluate FIDDLE by comparing it with state-of-the-art fidelity estimation techniques and routing optimization methods. The results demonstrate that our proposed surrogate model is able to provide a better estimation on the process fidelity compared to existing learning techniques, and our end-to-end framework significantly improves the process fidelity of quantum circuits across various noise models.

Political sectarianism is fueled in part by misperceptions of political opponents: People commonly overestimate the support for extreme policies among members of the other party. These misperceptions inflame partisan animosity and may be used to justify extremism among one's own party. Research suggests that correcting partisan misperceptions-by informing people about the actual views of outparty members-may reduce one's own expressed support for political extremism, including partisan violence and antidemocratic actions. However, there remains a limited understanding of how the design of correction interventions drives these effects. The present study investigated how correction effects depend on different representations of outparty views communicated through data visualizations. Building on prior interventions that present the average outparty view, we consider the impact of visualizations that more fully convey the range of views among outparty members. We conducted an experiment with U.S.-based participants from Prolific (N=239 Democrats, N=244 Republicans). Participants made predictions about support for political violence and undemocratic practices among members of their political outparty. They were then presented with data from an earlier survey on the actual views of outparty members. Some participants viewed only the average response (Mean-Only condition), while other groups were shown visual representations of the range of views from 75% of the outparty (Mean+Interval condition) or the full distribution of responses (Mean+Points condition). Compared to a control group that was not informed about outparty views, we observed the strongest correction effects (i.e., lower support for political violence and undemocratic practices) among participants in the Mean-only and Mean+Points condition, while correction effects were weaker in the Mean+Interval condition. In addition, participants who observed the full distribution of out-party views (Mean+Points condition) were most accurate at later recalling the degree of support among the outparty. Our findings suggest that data visualizations can be an important tool for correcting pervasive distortions in beliefs about other groups. However, the way in which variability in outparty views is visualized can significantly shape how people interpret and respond to corrective information. Supplemental materials for this paper are available at this OSF repository.

Group Fairness-aware Continual Learning (GFCL) aims to eradicate discriminatory predictions against certain demographic groups in a sequence of diverse learning tasks. This paper explores an even more challenging GFCL problem – how to sustain a fair classifier across a sequence of tasks with covariate shifts and unlabeled data. We propose the MacFRL solution, with its key idea to optimize the sequence of learning tasks. We hypothesize that high-confident learning can be enabled in the optimized task sequence, where the classifier learns from a set of prioritized tasks to glean knowledge, thereby becoming more capable to handle the tasks with substantial distribution shifts that were originally deferred. Theoretical and empirical studies substantiate that MacFRL excels among its GFCL competitors in terms of prediction accuracy and group fair-ness metrics.

Adverse clinical events related to unsafe care are among the top ten causes of death in the U.S. Accurate modeling and prediction of clinical events from electronic health records (EHRs) play a crucial role in patient safety enhancement. An example is modeling de facto care pathways that characterize common step-by-step plans for treatment or care. However, clinical event data pose several unique challenges, including the irregularity of time intervals between consecutive events, the existence of cycles, periodicity, multi-scale event interactions, and the high computational costs associated with long event sequences. Existing neural temporal point processes (TPPs) methods do not effectively capture the multi-scale nature of event interactions, which is common in many real-world clinical applications. To address these issues, we propose the cross-temporal-scale transformer (XTSFormer), specifically designed for irregularly timed event data. Our model consists of two vital components: a novel Feature-based Cycle-aware Time Positional Encoding (FCPE) that adeptly captures the cyclical nature of time, and a hierarchical multi-scale temporal attention mechanism, where different temporal scales are determined by a bottom-up clustering approach. Extensive experiments on several real-world EHR datasets show that our XTSFormer outperforms multiple baseline methods.

Deep neural networks often suffer performance drops when test data distribution differs from training data. Domain Generalization (DG) aims to address this by focusing on domain-invariant features or augmenting data for greater diversity. However, these methods often struggle with limited training domains or significant gaps between seen (training) and unseen (test) domains. To enhance DG robustness, we hypothesize that it is essential for the model to be trained on data from domains that closely resemble unseen test domains-an inherently difficult task due to the absence of prior knowledge about the unseen domains. Accordingly, we propose ConstStyle, a novel approach that leverages a unified domain to capture domain-invariant features and bridge the domain gap with theoretical analysis. During training, all samples are mapped onto this unified domain, optimized for seen domains. During testing, unseen domain samples are projected similarly before predictions. By aligning both training and testing data within this unified domain, ConstStyle effectively reduces the impact of domain shifts, even with large domain gaps or few seen domains. Extensive experiments demonstrate that ConstStyle consistently outperforms existing methods across diverse scenarios. Notably, when only a limited number of seen domains are available, ConstStyle can boost accuracy up to 19.82\% compared to the next best approach.

Understanding the inner workings of neural networks is essential for enhancing model performance and interpretability. Current research predominantly focuses on examining the connection between individual neurons and the model's final predictions, which suffers from challenges in interpreting the internal workings of the model, particularly when neurons encode multiple unrelated features. In this paper, we propose a novel framework that transitions the focus from analyzing individual neurons to investigating groups of neurons, shifting the emphasis from neuron-output relationships to the functional interactions between neurons. Our automated framework, NeurFlow, first identifies core neurons and clusters them into groups based on shared functional relationships, enabling a more coherent and interpretable view of the network’s internal processes. This approach facilitates the construction of a hierarchical circuit representing neuron interactions across layers, thus improving interpretability while reducing computational costs. Our extensive empirical studies validate the fidelity of our proposed NeurFlow. Additionally, we showcase its utility in practical applications such as image debugging and automatic concept labeling, thereby highlighting its potential to advance the field of neural network explainability.

Despite a plethora of anomaly detection models developed over the years, their ability to generalize to unseen anomalies remains an issue, particularly in critical systems. This paper aims to address this challenge by introducing Swift Hydra, a new framework for training an anomaly detection method based on generative AI and reinforcement learning (RL). Through featuring an RL policy that operates on the latent variables of a generative model, the framework synthesizes novel and diverse anomaly samples that are capable of bypassing a detection model. These generated synthetic samples are, in turn, used to augment the detection model, further improving its ability to handle challenging anomalies. Swift Hydra also incorporates Mamba models structured as a Mixture of Experts (MoE) to enable scalable adaptation of the number of Mamba experts based on data complexity, effectively capturing diverse feature distributions without increasing the model’s inference time. Empirical evaluations on ADBench benchmark demonstrate that Swift Hydra outperforms other state-of-the-art anomaly detection models while maintaining a relatively short inference time. From these results, our research highlights a new and auspicious paradigm of integrating RL and generative AI for advancing anomaly detection.

Federated Learning (FL) enables collaborative learning among clients via a coordinating server while avoiding direct data sharing, offering a perceived solution to preserve privacy. However, recent studies on Membership Inference Attacks (MIAs) have challenged this notion, showing high success rates against unprotected training data. While local differential privacy (LDP) is widely regarded as a gold standard for privacy protection in data analysis, most studies on MIAs either neglect LDP or fail to provide theoretical guarantees for attack success against LDP-protected data. To address this gap, we derive theoretical lower bounds for the success rates of low-polynomial-time MIAs that exploit vulnerabilities in fully connected or self-attention layers, regardless of the LDP mechanism used. We establish that even when data are protected by LDP, privacy risks persist, depending on the privacy budget. Practical evaluations on models like ResNet and Vision Transformer confirm considerable privacy risks, revealing that the noise required to mitigate these attacks significantly degrades models' utility.

This paper formalizes an emerging learning paradigm that uses a trained model as a reference to guide and enhance the training of a target model through strategic data selection or weighting, named **model steering**. While ad-hoc methods have been used in various contexts, including the training of large foundation models, its underlying principles remain insufficiently understood, leading to sub-optimal performance. In this work, we propose a theory-driven framework for model steering called **DRRho risk minimization**, which is rooted in Distributionally Robust Optimization (DRO). Through a generalization analysis, we provide theoretical insights into why this approach improves generalization and data efficiency compared to training without a reference model. To the best of our knowledge, this is the first time such theoretical insights are provided for the new learning paradigm, which significantly enhance our understanding and practice of model steering. Building on these insights and the connection between contrastive learning and DRO, we introduce a novel method for Contrastive Language-Image Pretraining (CLIP) with a reference model, termed DRRho-CLIP. Extensive experiments validate the theoretical insights, reveal a superior scaling law compared to CLIP without a reference model, and demonstrate its strength over existing heuristic approaches. Code is released at [github.com/Optimization-AI/DRRho-CLIP](https://github.com/Optimization-AI/DRRho-CLIP)

We study the Quality of Service Degradation (QoSD) problem, in which an adversary perturbs edge weights to degrade network performance. This setting arises in both network infrastructures and distributed ML systems, where communication quality, not just connectivity, determines functionality. While classical methods rely on combinatorial optimization, and recent ML approaches address only restricted linear variants with small-size networks, no prior model directly tackles the QoSD problem under nonlinear edge-weight functions. This work proposes Hephaestus, a self-reinforcing generative framework that synthesizes feasible solutions in latent space, to fill this gap. Our method includes three phases: (1) Forge: a Predictive Path-Stressing (PPS) algorithm that uses graph learning and approximation to produce feasible solutions with performance guarantee, (2) Morph: a new theoretically grounded training paradigm for Mixture of Conditional VAEs guided by an energy-based model to capture solution feature distributions, and (3) Refine: a reinforcement learning agent that explores this space to generate progressively near-optimal solutions using our designed differentiable reward function. Experiments on both synthetic and real-world networks show that our approach consistently outperforms classical and ML baselines, particularly in scenarios with nonlinear cost functions where traditional methods fail to generalize.

Contrastive Language-Image Pre-training (CLIP) generates versatile multimodal embeddings for diverse applications, yet the specific information captured within these representations is not fully understood. Current explainability techniques often target specific tasks, overlooking the rich, general semantics inherent in the representations. Our objective is to reveal the concepts encoded in CLIP embeddings by learning a surrogate representation, which is expressed as a linear combination of human-understandable concepts evident in the image. Our method, which we term EXPLAIN-R, introduces a novel approach that leverages CLIP's learned instance-instance similarity to train a surrogate model that faithfully mimics CLIP's behavior. From the trained surrogate, we derive concept scores for each input image; these scores quantify the contribution of each concept and act as the explanation for the representation. Quantitative evaluations on multiple datasets demonstrate our method's superior faithfulness over the baseline. Moreover, a user study confirms that our explanations are perceived as more relevant, complete, and useful. Our work provides a novel approach for interpreting CLIP image representations, enhancing the user interpretability of representations and fostering more trustworthy AI systems.

ABSTRACT Studying on networked systems, in which a communication between nodes is functional if their distance under a given metric is lower than a pre‐defined threshold, has received significant attention recently. In this work, we propose a metric to measure network resilience on guaranteeing the pre‐defined performance constraint. This metric is investigated under an optimization problem, namely length‐bounded paths interdiction in continuous domain ( cLPI ), which aims to identify a minimum set of nodes whose changes cause routing paths between nodes become undesirable for the network service. We show the problem is NP‐hard and propose a framework by designing two oracles, threshold blocking ( TB ) and critical path listing ( CPL ), which communicate back and forth to construct a feasible solution to cLPI with theoretical bicriteria approximation guarantees. Based on this framework, we propose two solutions for each oracle. Each combination of one solution to TB and one solution to CPL gives us a solution to cLPI . The bicriteria guarantee of our algorithms allows us to control the solutions' trade‐off between the returned size and the performance accuracy. New insights into the advantages of each solution are further discussed via experimental analysis.

In the last few years, many explanation methods based on the perturbations of input data have been introduced to shed light on the predictions generated by black-box models. The goal of this work is to introduce a novel perturbation scheme so that more faithful and robust explanations can be obtained. Our study focuses on the impact of perturbing directions on the data topology. We show that perturbing along the orthogonal directions of the input manifold better preserves the data topology, both in the worst-case analysis of the discrete Gromov-Hausdorff distance and in the average-case analysis via persistent homology. From those results, we introduce EMaP algorithm, realizing the orthogonal perturbation scheme. Our experiments show that EMaP not only improves the explainers' performance but also helps them overcome a recently developed attack against perturbation-based explanation methods.

Multiplex influence maximization (MIM) asks us to identify a set of seed users such as to maximize the expected number of influenced users in a multiplex network. MIM has been one of central research topics, especially in nowadays social networking landscape where users participate in multiple online social networks (OSNs) and their influences can propagate among several OSNs simultaneously. Although there exist a couple combinatorial algorithms to MIM, learning-based solutions have been desired due to its generalization ability to heterogeneous networks and their diversified propagation characteristics. In this paper, we introduce MIM-Reasoner, coupling reinforcement learning with probabilistic graphical model, which effectively captures the complex propagation process within and between layers of a given multiplex network, thereby tackling the most challenging problem in MIM. We establish a theoretical guarantee for MIM-Reasoner as well as conduct extensive analyses on both synthetic and real-world datasets to validate our MIM-Reasoner’s performance.

With the rapid adoption of Federated Learning (FL) as the training and tuning protocol for applications utilizing Large Language Models (LLMs), recent research highlights the need for significant modifications to FL to accommodate the large-scale of LLMs. While substantial adjustments to the protocol have been introduced as a response, comprehensive privacy analysis for the adapted FL protocol is currently lacking. To address this gap, our work delves into an extensive examination of the privacy analysis of FL when used for training LLMs, both from theoretical and practical perspectives. In particular, we design two active membership inference attacks with guaranteed theoretical success rates to assess the privacy leakages of various adapted FL configurations. Our theoretical findings are translated into practical attacks, revealing substantial privacy vulnerabilities in popular LLMs, including BERT, RoBERTa, DistilBERT, and OpenAI’s GPTs, across multiple real-world language datasets. Additionally, we conduct thorough experiments to evaluate the privacy leakage of these models when data is protected by state-of-the-art differential privacy (DP) mechanisms.

Federated Learning (FL) has garnered significant attention for its potential to protect user privacy while enhancing model training efficiency. For that reason, FL has found its use in various domains, from health care to industrial engineering, especially where data cannot be easily exchanged due to sensitive information or privacy laws. However, recent research has demonstrated that FL protocols can be easily compromised by active reconstruction attacks executed by dishonest servers. These attacks involve the malicious modification of global model parameters, allowing the server to obtain a verbatim copy of users' private data by inverting their gradient updates. Tackling this class of attack remains a crucial challenge due to the strong threat model. In this paper, we propose a defense mechanism, namely OASIS, based on image augmentation that effectively counteracts active reconstruction attacks while preserving model performance. We first uncover the core principle of gradient inversion that enables these attacks and theoretically identify the main conditions by which the defense can be robust regardless of the attack strategies. We then construct our defense with image augmentation showing that it can undermine the attack principle. Comprehensive evaluations demonstrate the efficacy of the defense mechanism highlighting its feasibility as a solution.

Fine-tuning pre-trained models is a popular approach in machine learning for solving complex tasks with moderate data. However, fine-tuning the entire pre-trained model is ineffective in federated data scenarios where local data distributions are diversely skewed. To address this, we explore integrating federated learning with a more effective prompt-tuning method, optimizing for a small set of input prefixes to reprogram the pre-trained model's behavior. Our approach transforms federated learning into a distributed set modeling task, aggregating diverse sets of prompts to globally fine-tune the pre-trained model. We benchmark various baselines based on direct adaptations of existing federated model aggregation techniques and introduce a new probabilistic prompt aggregation method that substantially outperforms these baselines. Our reported results on a variety of computer vision datasets confirm that the proposed method is most effective to combat extreme data heterogeneity in federated learning.

Federated learning is known to be vulnerable to both security and privacy issues. Existing research has focused either on preventing poisoning attacks from users or on concealing the local model updates from the server, but not both. However, integrating these two lines of research remains a crucial challenge since they often conflict with one another with respect to the threat model. In this work, we develop a principle framework that offers both privacy guarantees for users and detection against poisoning attacks from them. With a new threat model that includes both an honest-but-curious server and malicious users, we first propose a secure aggregation protocol using homomorphic encryption for the server to combine local model updates in a private manner. Then, a zero-knowledge proof protocol is leveraged to shift the task of detecting attacks in the local models from the server to the users. The key observation here is that the server no longer needs access to the local models for attack detection. Therefore, our framework enables the central server to identify poisoned model updates without violating the privacy guarantees of secure aggregation.

Recent development in the field of explainable artificial intelligence (XAI) has helped improve trust in Machine-Learning-as-a-Service (MLaaS) systems, in which an explanation is provided together with the model prediction in response to each query. However, XAI also opens a door for adversaries to gain insights into the black-box models in MLaaS, thereby making the models more vulnerable to several attacks. For example, feature-based explanations (e.g., SHAP) could expose the top important features that a black-box model focuses on. Such disclosure has been exploited to craft effective backdoor triggers against malware classifiers. To address this trade-off, we introduce a new concept of achieving local differential privacy (LDP) in the explanations, and from that we establish a defense, called XRand, against such attacks. We show that our mechanism restricts the information that the adversary can learn about the top important features, while maintaining the faithfulness of the explanations.

Bilevel optimization has been applied to a wide variety of machine learning models and numerous stochastic bilevel optimization algorithms have been developed in recent years. However, most existing algorithms restrict their focus on the single-machine setting so that they are incapable of handling the distributed data. To address this issue, under the setting where all participants compose a network and perform peer-to-peer communication in this network, we developed two novel decentralized stochastic bilevel optimization algorithms based on the gradient tracking communication mechanism and two different gradient estimators. Additionally, we established their convergence rates for nonconvex-strongly-convex problems with novel theoretical analysis strategies. To our knowledge, this is the first work achieving these theoretical results. Finally, we applied our algorithms to practical machine learning models, and the experimental results confirmed the efficacy of our algorithms.

Federated learning (FL) was originally regarded as a framework for collaborative learning among clients with data privacy protection through a coordinating server. In this paper, we propose a new active membership inference (AMI) attack carried out by a dishonest server in FL. In AMI attacks, the server crafts and embeds malicious parameters into global models to effectively infer whether a target data sample is included in a client’s private training data or not. By exploiting the correlation among data features through a non-linear decision boundary, AMI attacks with a certified guarantee of success can achieve severely high success rates under rigorous local differential privacy (LDP) protection; thereby exposing clients’ training data to significant privacy risk. Theoretical and experimental results on several benchmark datasets show that adding sufficient privacy-preserving noise to prevent our attack would significantly damage FL’s model utility.

Gaining a deep understanding of descriptive positive and negative norms, such as their propagation speed, is crucial due to their significant impact on shaping individuals' attitudes and actions towards specific beliefs. Unfortunately, conducting in-depth analysis related to the diffusion of descriptive norms is a complex and context-dependent phenomenon that is influenced by various factors over time. To address this gap, this paper presents a comprehensive analysis of norm propagation in online communities. The study encompasses structural and temporal analyses. Through these analyses, our objective is to uncover the dynamics of norm diffusion, comprehend the patterns of different norms within communities. This will provide valuable insights into the social context and dynamics surrounding positive and negative norms, ultimately enhancing our understanding of norm diffusion and social influence processes. This understanding also offers valuable insights for developing effective campaigns that promote positive norms.

Vertical Federated Learning (VFL) stands out as a promising approach to safeguard privacy in collaborative machine learning, allowing multiple entities to jointly train models on vertically partitioned datasets without revealing private information. While recent years have seen substantial research on privacy vulnerabilities and defense strategies for VFL, the focus has primarily been on passive scenarios where attackers adhere to the protocol. This perspective undermines the practical threats since the attackers can deviate from the protocol to improve their inference capabilities. To address this gap, our study introduces two innovative data reconstruction attacks designed to compromise data privacy in an active setting. Essentially, both attacks modify the gradients computed during the training phase of VFL to breach privacy. Our first attack uses an Active Inversion Network exploiting a small portion of known data in the training set to coerce the passive participants into training an auto-encoder for the reconstruction of their private data. The second attack, Active Generative Network, utilizes the knowledge of the training data distribution to guide the system into training a conditional generative network (C-GAN) for feature inferences. Our experiments confirm the efficacy of both attacks in inferring private features from real-world datasets.

Understanding the COVID-19 vaccine hesitancy, such as who and why, is very crucial since a large-scale vaccine adoption remains as one of the most efficient methods of controlling the pandemic. Such an understanding also provides insights into designing successful vaccination campaigns for future pandemics. Unfortunately, there are many factors involving in deciding whether to take the vaccine, especially from the cultural point of view. To obtain these goals, we design a novel culture-aware machine learning (ML) model, based on our new data collection, for predicting vaccination willingness. We further analyze the most important features which contribute to the ML model's predictions using advanced AI explainers such as the Probabilistic Graphical Model (PGM) and Shapley Additive Explanations (SHAP). These analyses reveal the key factors that most likely impact the vaccine adoption decisions. Our findings show that Hispanic and African American are most likely impacted by cultural characteristics such as religions and ethnic affiliation, whereas the vaccine trust and approval influence the Asian communities the most. Our results also show that cultural characteristics, rumors, and political affiliation are associated with increased vaccine rejection.

Quantum annealing (QA) has emerged as a powerful technique to solve optimization problems by taking advantages of quantum physics. In QA process, a bottleneck that may prevent QA to scale up is minor embedding step in which we embed optimization problems represented by a graph, called logical graph, to Quantum Processing Unit (QPU) topology of quantum computers, represented by another graph, call hardware graph. Existing methods for minor embedding require a significant amount of running time in a large-scale graph embedding. To overcome this problem, in this paper, we introduce a novel notion of adaptive topology which is an expandable sub graph of the hardware graph. From that, we develop a minor embedding algorithm, namely Adaptive TOpology eMbedding (ATOM). ATOM iteratively selects a node from the logical graph, and embeds it to the adaptive topology of the hardware graph. Our experimental results show that ATOM is able to provide a feasible embedding in much smaller running time than that of the state-of-the-art without compromising the quality of resulting embedding. 1

Recently, there has been significant interest in Temporal Graph Neural Networks (TGNN) because of their capability to learn from time-evolving graph-related data. However, similar to Graph Neural Network (GNN), explaining the predictions of TGNN is non-trivial due to its black-box and complex nature. A major approach for this problem in GNNs is by analyzing the model’s responses to some perturbations of the model’s inputs, called perturbation-based explanation methods. These methods are convenient and flexible as they do not require access to the model’s internals. However, a question arises: Does the lack of internal access limit these methods from uncovering crucial information about the predictions? Motivated by the question, this study explores the limitations of some popular classes of perturbation-based explanation methods. By constructing specific instances of TGNNs, we show (i) Node-perturbation is not reliable for identifying the paths that carry out the prediction, (ii) Edgeperturbation cannot reliably determine all the nodes contributing to the prediction and (iii) perturbing both nodes and edges does not consistently help identify the graph components responsible for the temporal aggregation in TGNNs. Our experimental results further demonstrate situations for failures of explanations can occur frequently in both synthetic and real-world scenarios. Thus, they emphasize the importance of perturbation choices and the internal information of the explained model in determining faithful explanations of the model’s predictions.

Influence maximization (IM) is formulated as selecting a set of initial users from a social network to maximize the expected number of influenced users. Researchers have made great progresses to design various traditional methods, yet both theoretical design and performance gain are close to their limits. In the past few years, learning-based IM methods have emerged to achieve stronger generalization ability to unknown graphs than traditional ones. However, the development of learning-based IM methods is still limited by fundamental obstacles, including 1) the difficulty of effectively solving the objective function; 2) the difficulty of characterizing the diversified and underlying diffusion patterns; and 3) the difficulty of adapting the solution under various node-centrality-constrained IM variants. To cope with the above challenges, we design a novel framework DeepIM to generatively characterize the latent representation of seed sets, and we propose to learn the diversified information diffusion pattern in a data-driven and end-to-end manner. Finally, we design a novel objective function to infer optimal seed sets under flexible node-centrality-based budget constraints. Extensive analyses are conducted over both synthetic and real-world datasets to demonstrate the overall performance of DeepIM.

This work, for the first time, introduces two constant factor approximation algorithms with linear query complexity for non-monotone submodular maximization over a ground set of size n subject to a knapsack constraint, DLA and RLA. DLA is a deterministic algorithm that provides an approximation factor of nearly 6 while RLA is a randomized algorithm with an approximation factor of nearly 4. Both run in linear query complexity. The key idea to obtain a constant approximation ratio with linear query lies in: (1) dividing the ground set into two appropriate subsets to find the near-optimal solution over these subsets with linear queries, and (2) combining a threshold greedy with properties of two disjoint sets or a random selection process to improve solution quality. In addition to the theoretical analysis, we have evaluated our proposed solutions with three applications: Revenue Maximization, Image Summarization, and Maximum Weighted Cut, showing that our algorithms not only return comparative results to state-of-the-art algorithms but also require significantly fewer queries.

Since 2016, sharding has become an auspicious solution to tackle the scalability issue in legacy blockchain systems. Despite its potential to strongly boost the blockchain throughput, sharding comes with its own security issues. To ease the process of deciding which shard to place transactions, existing sharding protocols use a hash-based transaction sharding in which the hash value of a transaction determines its output shard. Unfortunately, we show that this mechanism opens up a loophole that could be exploited to conduct a single-shard flooding attack, a type of Denial-of-Service (DoS) attack, to overwhelm a single shard that ends up reducing the performance of the system as a whole. To counter the single-shard flooding attack, we propose a countermeasure that essentially eliminates the loophole by rejecting the use of hash-based transaction sharding. The countermeasure leverages the Trusted Execution Environment (TEE) to let blockchain's validators securely execute a transaction sharding algorithm with a negligible overhead. We provide a formal specification for the countermeasure and analyze its security properties in the Universal Composability (UC) framework. Finally, a proof-of-concept is developed to demonstrate the feasibility and practicality of our solution.

Graph neural networks (GNNs) are susceptible to privacy inference attacks (PIAS) given their ability to learn joint representation from features and edges among nodes in graph data. To prevent privacy leakages in GNNs, we propose a novel heterogeneous randomized response (HeteroRR) mechanism to protect nodes’ features and edges against PIAS under differential privacy (DP) guarantees, without an undue cost of data and model utility in training GNNs. Our idea is to balance the importance and sensitivity of nodes’ features and edges in redistributing the privacy budgets since some features and edges are more sensitive or important to the model utility than others. As a result, we derive significantly better randomization probabilities and tighter error bounds at both levels of nodes’ features and edges departing from existing approaches, thus enabling us to maintain high data utility for training GNNs. An extensive theoretical and empirical analysis using benchmark datasets shows that HeteroRR significantly outperforms various baselines in terms of model utility under rigorous privacy protection for both nodes’ features and edges. That enables us to defend PIAs in DP-preserving GNNs effectively.

Temporal graph neural networks (TGNNs) have been widely used for modeling time-evolving graph-related tasks due to their ability to capture both graph topology dependency and non-linear temporal dynamic. The explanation of TGNNs is of vital importance for a transparent and trustworthy model. However, the complex topology structure and temporal depen-dency make explaining TGNN models very challenging. In this paper, we propose a novel explainer framework for TGNN models. Given a time series on a graph to be explained, the framework can identify dominant explanations in the form of a probabilistic graphical model in a time period. Case studies on the transportation domain demonstrate that the proposed approach can discover dynamic dependency structures in a road network for a time period.

Despite the great potential of Federated Learning (FL) in large-scale distributed learning, the current system is still subject to several privacy issues due to the fact that local models trained by clients are exposed to the central server. Consequently, secure aggregation protocols for FL have been developed to conceal the local models from the server. However, we show that, by manipulating the client selection process, the server can circumvent the secure aggregation to learn the local models of a victim client, indicating that secure aggregation alone is inadequate for privacy protection. To tackle this issue, we leverage blockchain technology to propose a verifiable client selection protocol. Owing to the immutability and transparency of blockchain, our proposed protocol enforces a random selection of clients, making the server unable to control the selection process at its discretion. We present security proofs showing that our protocol is secure against this attack. Additionally, we conduct several experiments on an Ethereum-like blockchain to demonstrate the feasibility and practicality of our solution.

Despite decades of development progress, the current e-voting systems still suffer several challenges especially in terms of security and privacy. Some of the key reasons behind this issue include lack of transparency in the process and inadequate privacy guarantee for the voters. To address these issues, we propose and construct zVote, a blockchain-based e-voting platform that aims to create a transparent and secure system for remote e-voting. With the use of blockchain, the voting result can be verifiable by the public, thereby eliminating the trust in the election authorities. Furthermore, we leverage homomorphic encryptions and zero-knowledge proofs to protect the privacy of users, especially in terms of anonymity and membership privacy, while retaining the correctness and verifiability of the votes. We provide formal definitions and proofs of the construction’s security. Our proof-of-concept implementation demonstrates the feasibility and practicality of zVote.

Ranking nodes based on their centrality stands a fundamental, yet, challenging problem in large-scale networks. Approximate methods can quickly estimate nodes' centrality and identify the most central nodes, but the ranking for the majority of remaining nodes may be meaningless. For example, ranking for less-known websites in search queries is known to be noisy and unstable. To this end, we investigate a new node ranking problem with two important distinctions: a) ranking quality, rather than the centrality estimation quality, as the primary objective; and b) ranking only nodes of interest, e.g., websites that matched search criteria. We propose Sample space Partitloning Hypothesis Ranking, or SaPHyRa, that transforms node rankinginto a hy-pothesis ranking in machine learning. This transformation maps nodes' centrality to the expected risks of hypotheses, opening doors for theoretical machine learning (ML) tools. The key of SaPHyRa is to partition the sample space into exact and approx-imate subspaces. The exact subspace contains samples related to the nodes of interest, increasing both estimation and ranking qualities. The approximate space can be efficiently sampled with ML-based techniques to provide theoretical guarantees on the estimation error. Lastly, we present SaPHyRabo an illustration of SaPHyRa on ranking nodes' betweenness centrality (BC). By combining a novel bi-component sampling, a 2-hop sample partitioning, and improved bounds on the Vapnik-Chervonenkis dimension, SaPHyRas., can effectively rank any node subset in BC. Its performance is up to 200x faster than state-of-the-art methods in approximating BC, while its rank correlation to the ground truth is improved by multifold.

Despite recent studies on understanding deep neural networks (DNNs), there exists numerous questions on how DNNs generate their predictions. Especially, given similar predictions on different inputs, are the underlying mechanisms generating those predictions the same? In this work, we propose NeuCEPT, a method to identify critical neurons that are important to the model’s local predictions and learn their underlying mechanisms. We first formulate a critical neurons identification problem as maximizing a sequence of mutual-information objectives and provide a theoretical framework to efficiently solve for critical neurons while keeping the precision under control. NeuCEPT next heuristically learns different model’s mechanisms in an unsupervised manner. Our experiments and case studies show that neurons identified by NeuCEPT not only have strong influence on the model’s predictions but also hold meaningful information about model’s mechanisms.

In this work, we study the problem of monotone non-submodular maximization with partition matroid constraint. Although a generalization of this problem has been studied in literature, our work focuses on leveraging properties of partition matroid constraint to (1) propose algorithms with theoretical bound and efficient query complexity; and (2) provide better analysis on theoretical performance guarantee of some existing techniques. We further investigate those algorithms' performance in two applications: Boosting Influence Spread and Video Summarization. Experiments show our algorithms return comparative results to the state-of-the-art algorithms while taking much fewer queries.

Bitcoin is the leading example of a blockchain application that facilitates peer-to-peer transactions without the need for a trusted third party. This paper considers possible attacks related to the decentralized network architecture of Bitcoin. We perform a data driven study of Bitcoin and present possible attacks based on spatial and temporal characteristics of its network. Towards that, we revisit the prior work, dedicated to the study of centralization of Bitcoin nodes over the Internet, through a fine-grained analysis of network distribution, and highlight the increasing centralization of the Bitcoin network over time. As a result, we show that Bitcoin is vulnerable to spatial, temporal, spatio-temporal, and logical partitioning attacks with an increased attack feasibility due to the network dynamics. We verify our observations through data-driven analyses and simulations, and discuss the implications of each attack on the Bitcoin network. We conclude with suggested countermeasures.

In wireless sensor networks (WSNs), especially in underwater sensor networks, the problem of reporting data to the sink with minimum latency has been widely discussed in many research works. Many studies address using data aggregation to report the same type of data to the sink without data collision in a short period of time. However, due to the rapid development of sensor technology in recent years, a sensor is allowed to have multiple sensing capabilities, that is, it can generate and collect different types of data. Because different types of data have different meanings and required aggregation functions, only the data that belong to the same type are allowed to be aggregated. In addition, due to the interference of the environment or noise, the links in the WSNs are often not bidirectional. This motivates us to study the problem of using minimum latency scheduling to aggregate and report data to the sink without data collision in multiple-data-type WSNs having unidirectional links, which is shown to be NP-hard in the article. The Relative-Collision-Graph-Based Scheduling Algorithm (RCGBSA) is proposed accordingly. Simulations are conducted to demonstrate the performance of the RCGBSA.

Knowledge Distillation (KD) has been considered as a key solution in model compression and acceleration in recent years. In KD, a small student model is generally trained from a large teacher model by minimizing the divergence between the probabilistic outputs of the two. However, as demonstrated in our experiments, existing KD methods might not transfer critical explainable knowledge of the teacher to the student, i.e. the explanations of predictions made by the two models are not consistent. In this paper, we propose a novel explainable knowledge distillation model, called XDistillation, through which both the performance the explanations’ information are transferred from the teacher model to the student model. The XDistillation model leverages the idea of convolutional autoencoders to approximate the teacher explanations. Our experiments shows that models trained by XDistillation outperform those trained by conventional KD methods not only in term of predictive accuracy but also faithfulness to the teacher models.

In many image-classification applications, understanding the reasons of model’s prediction can be as critical as the prediction’s accuracy itself. Various feature-based local explainers have been designed to provide explanations on the decision of complex classifiers. Nevertheless, there is no consensus on evaluating the quality of different explanations. In response to this lack of comprehensive evaluation, we introduce the c-Eval metric and its corresponding framework to quantify the feature-based local explanation’s quality. Given a classifier’s prediction and the corresponding explanation on that prediction, c-Eval is the minimum-distortion perturbation that successfully alters the prediction while keeping the explanation’s features unchanged. To show that c-Eval captures the importance of input’s features, we establish a connection between c-Eval and the features returned by explainers in affine and nearly-affine classifiers. We then introduce the c-Eval plot, which not only displays a strong connection between c-Eval and explainers’ quality, but also helps automatically determine explainer’s parameters.

Going beyond the one-type-fits-all design philosophy, the future 5G radio access network (RAN) with network slicing methodology is employed to support widely diverse applications over the same physical network. RAN slicing aims to logically split an infrastructure into a set of self-contained programmable RAN slices in which each slice built on top of the underlying physical RAN (substrate) is a separate logical mobile network and delivers a set of services with similar characteristics. Each RAN slice is constituted by various virtual network functions (VNFs) distributed geographically in numerous substrate nodes. A RAN configuration scheme for the network is imperative to embed VNFs in substrate nodes. In this paper, we propose to design new algorithms to enhance the stability of RAN slicing by addressing the resources allocation and VNF embedding problem, referred to as RS-configuration. Specifically, we establish the theoretical foundation for using RS-configuration to construct a VNF mapping plan for all VNFs with two efficient algorithms, including Group-based Algorithm (GBA) and Group-Connectivity-based Algorithm (GCBA). Through rigorous analysis and experimentation, we demonstrate that the proposed algorithms perform well within reasonable bounds of computational complexity.

Many research efforts recently have aimed at understanding the phenomenon of fake news, including recognizing their common features and patterns, leading to several fake news detection models based on machine learning. Yet, the real distinct strength of those models remains uncertain: some perform well only with particular data, but others are more general. Most of the models classified the fake news as a black-box without giving any explanations to users. In this work, therefore, we conduct an exploratory investigation that evaluates and interprets fake new detection models, including looking into which important features that contribute to the models’ prediction from the explainable machine learning perspective. This give us some insights on how the detection models work and their trustworthiness.

Although the iterative double auction has been widely used in many different applications, one of the major problems in its current implementations is that they rely on a trusted third party to handle the auction process. This imposes the risk of single point of failures, monopoly, and bribery. In this article, we aim to tackle this problem by proposing a novel decentralized and trustless framework for iterative double auction based on blockchain. Our design adopts the smart contract and state channel technologies to enable a double auction process among parties that do not need to trust each other, while minimizing the blockchain transactions. In specific, we propose an extension to the original concept of state channels that can support multiparty computation. Then, we provide a formal development of the proposed framework and prove the security of our design against adversaries. Finally, we develop a proof-of-concept implementation of our framework using Elixir and Solidity, on which we conduct various experiments to demonstrate its feasibility and practicality.

Graph mining plays a pivotal role across a number of disciplines, and a variety of algorithms have been developed to answer who/what type questions. For example, what items shall we recommend to a given user on an e-commerce platform? The answers to such questions are typically returned in the form of a ranked list, and graph-based ranking methods are widely used in industrial information retrieval settings. However, these ranking algorithms have a variety of sensitivities, and even small changes in rank can lead to vast reductions in product sales and page hits. As such, there is a need for tools and methods that can help model developers and analysts explore the sensitivities of graph ranking algorithms with respect to perturbations within the graph structure. In this paper, we present a visual analytics framework for explaining and exploring the sensitivity of any graph-based ranking algorithm by performing perturbation-based what-if analysis. We demonstrate our framework through three case studies inspecting the sensitivity of two classic graph-based ranking algorithms (PageRank and HITS) as applied to rankings in political news media and social networks.

Relation Extraction (RE) is one of the fundamental tasks in Information Extraction. The goal of this task is to find the semantic relations between entity mentions in text. It has been shown in many previous work that the structure of the sentences (i.e., dependency trees) can provide important information/features for the RE models. However, the common limitation of the previous work on RE is the reliance on some external parsers to obtain the syntactic trees for the sentence structures. On the one hand, it is not guaranteed that the independent external parsers can offer the optimal sentence structures for RE and the customized structures for RE might help to further improve the performance. On the other hand, the quality of the external parsers might suffer when applied to different domains, thus also affecting the performance of the RE models on such domains. In order to overcome this issue, we introduce a novel method for RE that simultaneously induces the structures and predicts the relations for the input sentences, thus avoiding the external parsers and potentially leading to better sentence structures for RE. Our general strategy to learn the RE-specific structures is to apply two different methods to infer the structures for the input sentences (i.e., two views). We then introduce several mechanisms to encourage the structure and semantic consistencies between these two views so the effective structure and semantic representations for RE can emerge. We perform extensive experiments on the ACE 2005 and SemEval 2010 datasets to demonstrate the advantages of the proposed method, leading to the state-of-the-art performance on such datasets.

Maximizing on k-submodular functions subject to size constraint has received extensive attention recently. In this paper, we investigate a more realistic scenario of this problem that (1) obtaining exact evaluation of an objective function is impractical, instead, its noisy version is acquired; and (2) algorithms are required to take only one single pass over dataset, producing solutions in a timely manner. We propose two novel streaming algorithms, namely DStream and RStream, with their theoretical performance guarantees. We further demonstrate the efficiency of our algorithms in two application, showing that our algorithms can return comparative results to state-of-the-art non-streaming methods while using a much fewer number of queries.

In this paper, we aim to develop a scalable algorithm to preserve differential privacy (DP) in adversarial learning for deep neural networks (DNNs), with certified robustness to adversarial examples. By leveraging the sequential composition theory in DP, we randomize both input and latent spaces to strengthen our certified robustness bounds. To address the trade-off among model utility, privacy loss, and robustness, we design an original adversarial objective function, based on the post-processing property in DP, to tighten the sensitivity of our model. A new stochastic batch training is proposed to apply our mechanism on large DNNs and datasets, by bypassing the vanilla iterative batch-by-batch training in DP DNNs. An end-to-end theoretical analysis and evaluations show that our mechanism notably improves the robustness and scalability of DP DNNs.

In Graph Neural Networks (GNNs), the graph structure is incorporated into the learning of node representations. This complex structure makes explaining GNNs' predictions become much more challenging. In this paper, we propose PGM-Explainer, a Probabilistic Graphical Model (PGM) model-agnostic explainer for GNNs. Given a prediction to be explained, PGM-Explainer identifies crucial graph components and generates an explanation in form of a PGM approximating that prediction. Different from existing explainers for GNNs where the explanations are drawn from a set of linear functions of explained features, PGM-Explainer is able to demonstrate the dependencies of explained features in form of conditional probabilities. Our theoretical analysis shows that the PGM generated by PGM-Explainer includes the Markov-blanket of the target prediction, i.e. including all its statistical information. We also show that the explanation returned by PGM-Explainer contains the same set of independence statements in the perfect map. Our experiments on both synthetic and real-world datasets show that PGM-Explainer achieves better performance than existing explainers in many benchmark tasks.

Recently, a rapid growth in the number of vehicles on the road has led to an unexpected surge of parking demand. Consequently, finding a parking space has become increasingly difficult and expensive. One of the viable approaches is to utilize both public and private parking lots (PLs) to effectively share the parking spaces. However, when the parking demands are not balanced among PLs, a local congestion problem occurs where some PLs are overloaded, and others are underutilized. Therefore, in this article, we formulate the parking assignment problem with two objectives: 1) minimizing parking expenses and 2) balancing parking demand among multiple PLs. First, we derive a matching solution for minimizing parking expenses. Then, we extend our study by considering both parking expenses and balancing parking demand, formulating this as a mixed-integer linear programming problem. We solve that problem by using an alternating direction method of multipliers (ADMM)-based algorithm that can enable a distributed implementation. Finally, the simulation results show that the matching game approach outperforms the greedy approach by 8.5% in terms of parking utilization, whereas the ADMM-based algorithm produces performance gains up to 27.5% compared with the centralized matching game approach. Furthermore, the ADMM-based proposed algorithm can obtain a near-optimal solution with a fast convergence that does not exceed eight iterations for the network size with 1000 vehicles. Note to Practitioners-The efficiency of the parking assignment is critical to the parking management systems in order to provide the best parking guides. This article investigates the cost minimization problem for parking assignment while balancing parking demand among multiple parking lots (PLs). Previous parking assignment approaches do not jointly investigate the cost of parking and the cost of PL utilization. Therefore, they can fail to the local congestion problem caused by a large number of vehicles driving toward the same PL. In this article, a new method that considers both of minimizing parking expenses and balancing parking demand is proposed. It is obtained by using the alternating direction method of multipliers (ADMM)-based proposal that distributively solves a constrained optimization problem. Based on the experimental results, the ADMM-based algorithm outperforms the matching-based algorithm and the greedy algorithm in terms of the balancing parking demand and reducing parking expenses. The proposed method can be readily implemented in real-world industrial PLs. In the future work where parking assignments for electric vehicles are needed, our proposed mechanism can then be extended to solve the balanced electricity overload multiple charging stations.

Fake news and rumors constitute a major problem in social networks recently. Due to the fast information propagation in social networks, it is inefficient to use human labor to detect suspicious news. Automatic rumor detection is thus necessary to prevent devastating effects of rumors on the individuals and society. Previous work has shown that in addition to the content of the news/posts and their contexts (i.e., replies), the relations or connections among those components are important to boost the rumor detection performance. In order to induce such relations between posts and contexts, the prior work has mainly relied on the inherent structures of the social networks (e.g., direct replies), ignoring the potential semantic connections between those objects. In this work, we demonstrate that such semantic relations are also helpful as they can reveal the implicit structures to better capture the patterns in the contexts for rumor detection. We propose to employ the self-attention mechanism in neural text modeling to achieve the semantic structure induction for this problem. In addition, we introduce a novel method to preserve the important information of the main news/posts in the final representations of the entire threads to further improve the performance for rumor detection. Our method matches the main post representations and the thread representations by ensuring that they predict the same latent labels in a multitask learning framework. The extensive experiments demonstrate the effectiveness of the proposed model for rumor detection, yielding the state-of-the-art performance on recent datasets for this problem.

Utility providers are gradually deploying social networks as a useful addition to the Smart Grid in order to help engage consumers in energy management and efficient usage. Besides its benefits, is there any negative impact to the Smart Grid? In this paper, we investigate the vulnerability of Smart Grid when integrating into social networks, where attackers utilize misinformation propagation in social network to alter electricity customer's behavior with the goal of causing degradation to power infrastructure. Stand in both perspectives of power facility administrator and adversary, we model the vulnerability assessment of the system under an optimization problem, which enables us to provide theoretical analysis and behavior investigation of the system based on the complexity theory. As solving the problem is challenging, we propose heuristic solutions and show their efficiency on assessing the system's vulnerability in the presence of misinformation attacks. Therefore, we conclude that misinformation attacks must be considered when developing the security model for Socially-enabled Smart Grid technology and planning mitigation techniques.

A major challenge in blockchain sharding protocols is that more than 95% transactions are cross-shard. Not only those cross-shard transactions degrade the system throughput but also double the confirmation time, and exhaust an already scarce network bandwidth. Are cross-shard transactions imminent for sharding schemes? In this paper, we propose a new sharding paradigm, called OptChain, in which cross-shard transactions are minimized, resulting in almost twice faster confirmation time and throughput. By treating transactions as a stream of nodes in an online graph, OptChain utilizes a lightweight and on-the-fly transaction placement method to group both related and soon-related transactions into the same shards. At the same time, OptChain maintains a temporal balance among shards to guarantee the high parallelism. Our comprehensive and large-scale simulation using Oversim P2P library confirms a significant boost in performance with up to 10 folds reduction in cross-shard transactions, more than twice reduction in confirmation time, and 50% increase in throughput. When combined with Omniledger sharding protocol, OptChain delivers a 6000 transactions per second throughput with 10.5s confirmation time.

Motivated by various settings, we study a new Influence Maximization problem at the Community level (IMC) which aims at finding k users to maximize the benefit of influenced communities where a community is influenced iff the number of influenced users belong to this community exceeds its predefined threshold. In general, IMC objective function is not submodular nor supermodular, thereby making it very challenging to apply existing greedy solutions of the classic influence maximization (IM) where submodular function is required. Furthermore, the major challenge in the traditional methods for any related IM problem is the inefficiency in estimating the influence spread. IMC brings this difficulty to a higher level when considering influenced communities instead of influencing each individual user. In this paper, we propose different approximation algorithms for IMC: (1) Using Sandwich approach with a tight submodular function to bound the IMC objective function, (2) Activating the top-k influencing nodes found from network sampling. Furthermore, when the activated thresholds of communities are bounded by a constant, we propose an algorithm with performance guarantee tight to the inapproximability of IMC assuming the exponential time hypothesis. Each algorithm has its own strengths in a trade-off between effectiveness and running time, which are illustrated both in theory and comprehensive experimental evaluation.

Bitcoin is the leading example of a blockchain application that facilitates peer-to-peer transactions without the need for a trusted intermediary. This paper considers possible attacks related to the decentralized network architecture of Bitcoin. We perform a data driven study of Bitcoin and present possible attacks based on spatial and temporal characteristics of its network. Towards that, we revisit the prior work, dedicated to the study of centralization of Bitcoin nodes over the Internet, through a fine-grained analysis of network distribution, and highlight the increasing centralization of the Bitcoin network over time. As a result, we show that Bitcoin is vulnerable to spatial, temporal, spatio-temporal, and logical partitioning attacks with an increased attack feasibility due to network dynamics. We verify our observations by simulating attack scenarios and the implications of each attack on the Bitcoin . We conclude with suggested countermeasures.

Bayesian optimization (BO) is concerned with efficient optimization using probabilistic methods. Predictive entropy search (PES) is a popular and successful BO strategy to find a point that maximizes the information gained about the optima location of an unknown function. Since the PES analytical form is intractable, it requires approximations and is computationally expensive. These approximations may degrade PES performance in terms of accuracy and efficiency. In this paper, we propose an alternative scheme - predictive variance reduction search (PVRS) - to find a point that maximally reduces the uncertainty at the perceived optima locations. The optimization converges to the true optimum when the uncertainty at all perceived optima locations is vanished. Our novel modification is beneficial in two ways. First, PVRS can be computed in closed-form, unlike the approximations made in PES. Second, PVRS is simple and easy to implement. As a result, the proposed PVRS gains huge speed up for scalable BO whilst showing favorable optimization efficiency. Furthermore, we extend our PVRS framework for batch setting where we select multiple experiments for parallel evaluations at each iteration. Empirically, we demonstrate the effectiveness of the PVRS on both benchmark functions and real-world applications in standard and batch BO settings.

In this work, we study the Submodular Cost Submodular Cover problem, which is to minimize the submodular cost required to ensure that the submodular benefit function exceeds a given threshold. Existing approximation ratios for the greedy algorithm assume a value oracle to the benefit function. However, access to a value oracle is not a realistic assumption for many applications of this problem, where the benefit function is difficult to compute. We present two incomparable approximation ratios for this problem with an approximate value oracle and demonstrate that the ratios take on empirically relevant values through a case study with the Influence Threshold problem in online social networks.

In this paper, we propose a novel Heterogeneous Gaussian Mechanism (HGM) to preserve differential privacy in deep neural networks, with provable robustness against adversarial examples. We first relax the constraint of the privacy budget in the traditional Gaussian Mechanism from (0, 1] to (0, infty), with a new bound of the noise scale to preserve differential privacy. The noise in our mechanism can be arbitrarily redistributed, offering a distinctive ability to address the trade-off between model utility and privacy loss. To derive provable robustness, our HGM is applied to inject Gaussian noise into the first hidden layer. Then, a tighter robustness bound is proposed. Theoretical analysis and thorough evaluations show that our mechanism notably improves the robustness of differentially private deep neural networks, compared with baseline approaches, under a variety of model attacks.

In this paper, a novel economic approach, based on the framework of contract theory, is proposed for providing incentives for LTE over unlicensed channels (LTE-U) in cellular networks. In this model, a mobile network operator (MNO) designs and offers a set of contracts to the users to motivate them to accept being served over the unlicensed bands. A practical model in which the information about the quality-of-service (QoS) required by every user is not known to the MNO and other users is considered. For this contractual model, the closed-form expression of the price charged by the MNO for every user is derived and the problem of spectrum allocation is formulated as a matching game with incomplete information. For the matching problem, a distributed algorithm is proposed to assign the users to the licensed and unlicensed spectra. The simulation results show that the proposed pricing mechanism can increase the fraction of users that achieve their QoS requirements by up to 45% compared to classical algorithms that do not account for users requirements. Moreover, the performance of the proposed algorithm in the case of incomplete information is shown to approach the performance of the same mechanism with complete information.

Device-to-device (D2D) communication is a promising approach to efficiently disseminate critical or viral information. Reaping the benefits of D2D-enabled networks is contingent upon choosing the optimal content dissemination policy subject to resource and user distribution constraints. In this paper, a novel D2D network analysis framework is proposed to study the impacts of frequently occurring subgraphs, known as motifs, on D2D network performance and to determine an effective content dissemination strategy. In the proposed framework, the distribution of devices in the D2D network is modeled as a Thomas cluster process (TCP), and two graph structures, the star, and chain motifs, are studied in the communication graph. Based on the properties of the TCP, the closed-form analytical expressions for the statistical significance, the outage probability, as well as the average throughput per device, are derived. The simulation results corroborate the analytical derivations and show the influence of different system topologies on the occurrence of motifs and the D2D system throughput. More importantly, the results highlight that, as the statistical significance of motifs increases, the system throughput will initially increase and then subsequently decreases. Hence, the network operators can obtain statistical significance regions for chain and star motifs that map to the optimal content dissemination performance. Furthermore, using the obtained regions and the analytical expressions for statistical significance, network operators can effectively identify which clusters of devices can be leveraged for D2D communications while determining the number of serving devices in each identified cluster.

In this paper, we study the cost-aware target viral marketing (CTVM) problem, a generalization of influence maximization. CTVM asks for the most cost-effective users to influence the most relevant users. In contrast to the vast literature, we attempt to offer exact solutions. As the problem is NP-hard, thus, exact solutions are intractable, we propose TipTop, a$(1-\epsilon)$-optimal solution for arbitrary$\epsilon >0$that scales to very large networks, such as Twitter. At the heart of TipTop lies an innovative technique that reduces the number of samples as much as possible. This allows us to exactly solve CTVM on a much smaller space of generated samples using integer programming. Furthermore, TipTop lends a tool for researchers to benchmark their solutions against the optimal one in large-scale networks, which is currently not available.

Device-to-device (D2D) communication has recently gained much attention for its potential to boost the capacity of cellular systems. D2D enables direct communication between devices while bypassing a base station (BS), hence decreasing the load of BSs and increasing the network throughput via spatial reuse of radio resources. However, the cellular system is highly dynamic, an optimal allocation plan of radio resource to D2D links at one time point can easily become suboptimal when devices move. Thus, to maximize spatial reuse in cellular systems, it is crucial to update the resource allocation adaptively to reflect the current system status. In this paper, we develop the first adaptive solution framework to the dynamic resource problem for maximizing spatial reuse. At the core of the framework, we present the two algorithms for the adaptive set multicover problem with approximation ratio f and log n, respectively, where f is the frequency of the most frequent element and n is the total number of elements. The experimental results not only show that our solutions have a significant improvement in running time, compared with optimal or approximated offline methods, but also demonstrate their good performance through the resource usage, network throughput, and other metrics.

While online social networks (OSNs) have become an important platform for information exchange, the abuse of OSNs to spread misinformation has become a significant threat to our society. To restrain the propagation of misinformation in its early stages, we study the Distance-constrained Misinformation Combat under Uncertainty problem, which aims to both reduce the spread of misinformation and enhance the spread of correct information within a given propagation distance. The problem formulation considers the competitive diffusion of misinformation and correct information. It also accounts for the uncertainty in identifying initial misinformation adopters. For competitive propagation with major-threshold activation, we propose a solution based on stochastic programming and provide an upper-bound in the presence of uncertainty. We propose an efficient Combat Seed Selection algorithm to tackle general-threshold activation, in which we define a measure, “effectiveness”, to evaluate the contribution of nodes to the fight against misinformation. Through extensive experiments, we validate that our algorithm outputs high-quality solution with very fast computation.

In this paper, we highlight a new form of distributed denial of service (DDoS) attack that impacts the memory pools of cryptocurrency systems causing massive transaction backlog and higher mining fees. Towards that, we study such an attack on Bitcoin mempools and explore its effects on the mempool size and transaction fees paid by the legitimate users. We also propose countermeasures to contain such an attack. Our countermeasures include fee-based and age-based designs, which optimize the mempool size and help to counter the effects of DDoS attacks. We evaluate our designs using simulations in diverse attack conditions.

As the existing power grid becomes increasingly complex, the deployment of Smart Grids-which can significantly improve the stability and efficiency of power infrastructure-has seen increasing interest. However, with new technology comes new security concerns. Recent work has shown that fabricating valid but malicious messages on a Smart Grid's SCADA network can cause widespread power outages. Moreover, the large scale, complexity, and tight constraints of these networks makes deploying in-line detection systems insufficient. A common approach is to instead conduct whole-network audits by temporarily duplicating & forwarding all network traffic to a server dedicated to detecting malicious content. This is usually done by taking advantage of port- mirroring to duplicate the packets received with minimal overhead. However, the operation of these audits sees a number of challenges. For instance, each router used to collect traffic demands physical set-up - thus there is a real cost to needlessly high coverage. In this work, we consider the problem ofefficientlyfinding the minimal set of routers in the SCADA network to use for auditing traffic. This efficiency is critical for enabling timely auditing. Similar versions of this problem have seen study. However, they suffer either from a severe mismatch w.r.t. the problem domain, or from serious scalability concerns. This motivates us to devise a novel (2+θ)(ln|V|+1) approximation algorithm for this problem with a 2-approximation for the special case of tree networks. We experimentally evaluate our solution and compare it to an optimal IP formulation, finding that it performs near-optimally on small networks and significantly outperforms heuristics in all cases.

The vulnerability of interdependent networks has recently drawn much attention, especially in the key infrastructure networks such as power and communication networks. However, the existing works mainly considered a single cascade model across the networks and there is a need for more accurate models and analysis. In this paper, we focus on the interdependent power/communication networks to accurately analyze their vulnerability by considering heterogeneous cascade models. Accurately analyzing interdependent networks is challenging as the cascades are heterogeneous yet interdependent. Also, including multiple timescales into the context can further increase the complexity. To better depict the vulnerability of interdependent networks, we first propose a method to learn a threshold model from historical data to characterize the cascades in the power network and alleviate the need of calculating complicated power network dynamics. Next, we introduce message passing equations to generalize the threshold model in the power network and the percolation model in the communication network, based on which we derive efficient solution for finding the most critical nodes in the interdependent networks. Removing the most critical nodes can cause the largest cascade and thus characterizes the vulnerability. We evaluate the performance of the proposed methods in various datasets and discuss how network parameters, such as the timescales, can impact the vulnerability.

The optimization of submodular functions on the integer lattice has received much attention recently, but the objective functions of many applications are non-submodular. We provide two approximation algorithms for maximizing a non-submodular function on the integer lattice subject to a cardinality constraint; these are the first algorithms for this purpose that have polynomial query complexity. We propose a general framework for influence maximization on the integer lattice that generalizes prior works on this topic, and we demonstrate the efficiency of our algorithms in this context.

In this work, we examine the problem of adaptively uncovering network topology in an incomplete network, to support more accurate decision making in various real-world applications, such as modeling for reconnaissance attacks and network probing. While this problem has been partially studied, we provide a novel take on it by modeling it with a set of crawlers termed “bots” which can uncover independent portions of the network in parallel. Accordingly, we develop three adaptive algorithms, which make decisions based on previous observations due to incomplete information, namely AGP, a sequential method; FastAGP, a parallel algorithm; and ALSP, an extension of FastAGP uses local search to improve guarantees. These algorithms are proven to have 1/3, 1/7, and 1/ (5 + ϵ) approximation ratios, respectively. The key analysis of these algorithms is the connection between adaptive algorithms and an intersection of multiple partition matroids. We conclude with an evaluation of these algorithms to quantify the impact of both adaptivity and parallelism. We find that in practice, adaptive approaches perform significantly better, while FastAGP performs nearly as well as AGP in most cases despite operating in a massively parallel fashion. Finally, we show that a balance between the quantity and quality of bots is ideal for maximizing observation of the network.

Previous approaches to caching for Device-to-Device (D2D) communication cache popular files during off-peak hours. Since the popularity of content may evolve quickly or be unavailable in advance, we propose a flexible approach to cellular device caching where files are cached or uncached dynamically as file popularity evolves Dynamic caching motivates a space-efficient optimization problem Minimum File Placement (MFP), which is to cache a single file in the least amount of cache space to ensure a specified cache hit rate. In order to estimate the future cache hit rate, we use historical heterogeneous contact and request patterns of the devices. We present a bicriteria greedy algorithm for MFP and incorporate this algorithm into a dynamic approach to caching from a library of files with evolving popularity distribution. In an extensive experimental evaluation, we analyze the effectiveness of our approach to mobile device caching and demonstrate its advantages over other static contact-pattern-aware caching and alternative dynamic approaches.

Signaling networks are involved in almost all major diseases such as cancer. As a result of this, understanding how signaling networks function is vital for finding new treatments for many diseases. Using gene knockdown assays such as RNA interference (RNAi) technology, many genes involved in these networks can be identified. However, determining the interactions between these genes in the signaling networks using only experimental techniques is very challenging, as performing extensive experiments is very expensive and sometimes, even impractical. Construction of signaling networks from RNAi data using computational techniques have been proposed as an alternative way to solve this challenging problem. However, the earlier approaches are either not scalable to large scale networks, or their accuracy levels are not satisfactory. In this study, we integrate RNAi data given on a target network with multiple reference signaling networks and phylogenetic trees to construct the topology of the target signaling network. In our work, the network construction is considered as finding the minimum number of edit operations on given multiple reference networks, in which their contributions are weighted by their phylogenetic distances to the target network. The edit operations on the reference networks lead to a target network that satisfies the RNAi knockdown observations. Here, we propose two new reference-based signaling network construction methods that provide optimal results and scale well to large-scale signaling networks of hundreds of components. We compare the performance of these approaches to the state-of-the-art reference-based network construction method SiNeC on synthetic, semi-synthetic, and real datasets. Our analyses show that the proposed methods outperform SiNeC method in terms of accuracy. Furthermore, we show that our methods function well even if evolutionarily distant reference networks are used. Application of our methods to the Apoptosis and Wnt signaling pathways recovers the known protein-protein interactions and suggests additional relevant interactions that can be tested experimentally.

Motivated by online social networks that are linked together through overlapping users, we study the influence maximization problem on a multiplex, with each layer endowed with its own model of influence diffusion. This problem is a novel version of the influence maximization problem that necessitates new analysis incorporating the type of propagation on each layer of the multiplex. We identify a new property, generalized deterministic submodular, which when satisfied by the propagation in each layer, ensures that the propagation on the multiplex overall is submodular-for this case, we formulate influential seed finder (ISF), the greedy algorithm with approximation ratio (1-1/e). Since the size of a multiplex comprising multiple OSNs may encompass billions of users, we formulate an algorithm knapsack seeding of network (KSN) that runs on each layer of the multiplex in parallel. KSN takes an α-approximation algorithm A for the influence maximization problem on a single network as input, and has approximation ratio ((1 - ϵ)α)/((o + 1)k) for arbitrary ϵ > 0, o is the number of overlapping users, and k is the number of layers in the multiplex. Experiments on real and synthesized multiplexes validate the efficacy of the proposed algorithms for the problem of influence maximization in the heterogeneous multiplex. Implementations of ISF and KSN are available at http://www.alankuhnle.com/papers/mim/mim.html.

In assessing privacy on online social networks, it is important to investigate their vulnerability to reconnaissance strategies, in which attackers lure targets into being their friends by exploiting the social graph in order to extract victims' sensitive information. As the network topology is only partially revealed after each successful friend request, attackers need to employ an adaptive strategy. Existing work only considered a simple strategy in which attackers sequentially acquire one friend at a time, which causes tremendous delay in waiting for responses before sending the next request, and which lack the ability to retry failed requests after the network has changed. In contrast, we investigate an adaptive and parallel strategy, of which attackers can simultaneously send multiple friend requests in batch and recover from failed requests by retrying after topology changes, thereby significantly reducing the time to reach the targets and greatly improving robustness. We cast this approach as an optimization problem, Max-Crawling, and show it inapproximable within (1 - 1/e + ε). We first design our core algorithm PM-AReST which has an approximation ratio of (1 - e-(1-1/e)) using adaptive monotonic submodular properties. We next tighten our algorithm to provide a nearoptimal solution, i.e. having a ratio of (1 - 1/e), via a two-stage stochastic programming approach. We further establish the gap bound of (1 - e-(1-1/e)2) between batch strategies versus the optimal sequential one. We experimentally validate our theoretical results, finding that our algorithm performs nearoptimally in practice and that this is robust under a variety of problem settings.

Motivated by the relevance of clustering or transitivity to a variety of network applications, we study the Triangle Interdiction Problem (TIP), which is to find a minimum-size set of edges that intersects all triangles of a network. As existing approximation algorithms for this NP-hard problem either do not scale well to massive networks or have poor solution quality, we formulate two algorithms, TARL and DART, with worst-case guarantees 5/2 and 3 with respect to optimal, respectively. Furthermore, DART is able to efficiently maintain its worst-case guarantee under dynamic edge insertion and removal to the network. In our comprehensive experimental evaluation, we demonstrate that DART is able to run on networks with billions of triangles within 2 hours and is able to dynamically update its solution in microseconds.

Online Social Networks (OSNs) are effective platforms for viral marketing. Due to their importance, viral marketing related problems in OSNs have been extensively studied in the past decade. However, none of the existing works can cope with the situation that the propagation rate dynamically increases for popular topics, as they all assume known propagation rates. In this paper, to better describe realistic information propagation in OSNs, we propose a novel model, Dynamic Influence Propagation (DIP), that allows propagation rate to change during the diffusion. We then define a new research problem: Threshold Activation Problem under DIP (TAP-DIP) to study the impact of DIP. TAP-DIP adds extra complexity on the already #P-hard TAP problem. Despite it hardness, we are able to approximate TAP-DIP with O(log|V|) ratio. Sitting in the core of our algorithm are the Lipschitz optimization technique and a novel solution to the general version of TAP, the Multi-TAP problem. Using various real OSN datasets, we experimentally demonstrate the impact of DIP and that our solution not only generates high-quality seed sets when being aware of the rate increase, but also is scalable.

We consider the Threshold Activation Problem (TAP): given social network G and positive threshold T, find a minimum-size seed set A that can trigger expected activation of at least T. We introduce the first scalable, parallelizable algorithm with performance guarantee for TAP suitable for datasets with millions of nodes and edges; we exploit the bicriteria nature of solutions to TAP to allow the user to control the running time versus accuracy of our algorithm through a parameter α ϵ (0, 1): given η > 0, with probability 1 - η our algorithm returns a solution A with expected activation greater than T - 2αT, and the size of the solution A is within factor 1-h 4α/T + log(T) of the optimal size. The algorithm runs in time O (α-2log (n/η) (n + m)|A|), where n, m, refer to the number of nodes, edges in the network. The performance guarantee holds for the general triggering model of internal influence and also incorporates external influence, provided a certain condition is met on the cost-effectivity of seed selection.

One of the most central problems in viral marketing is Influence Maximization (IM), which finds a set of k seed users who can influence the maximum number of users in online social networks. Unfortunately, all existing algorithms to IM, including the state of the art SSA and IMM, have an approximation ratio of (1 - 1/e - ϵ). Recently, a generalization of IM, Cost-aware Target Viral Marketing (CTVM), asks for the most cost-effective users to influence the most relevant users, has been introduced. The current best algorithm for CTVM has an approximation ratio of (1 - 1/√e - ϵ). In this paper, we study the CTVM problem, aiming to optimally solve the problem. We first highlight that using a traditional two stage stochastic programming to exactly solve CTVM is not possible because of scalability. We then propose an almost exact algorithm TIPTOP, which has an approximation ratio of (1 - ϵ). This result significantly improves the current best solutions to both IM and CTVM. At the heart of TIPTOP lies an innovative technique that reduces the number of samples as much as possible. This allows us to exactly solve CTVM on a much smaller space of generated samples using Integer Programming. While obtaining an almost exact solution, TIPTOP is very scalable, running on billion-scale networks such as Twitter under three hours. Furthermore, TIPTOP lends a tool for researchers to benchmark their solutions against the optimal one in large-scale networks, which is currently not available.

Online social networks have been one of the most effective platforms for marketing and advertising. Through the “world-of-mouth” exchanges, so-called viral marketing, the influence and product adoption can spread from few key influencers to billions of users in the network. To identify those key influencers, a great amount of work has been devoted for the influence maximization (IM) problem that seeks a set of k seed users that maximize the expected influence. Unfortunately, IM encloses two impractical assumptions: 1) any seed user can be acquired with the same cost and 2) all users are equally interested in the advertisement. In this paper, we propose a new problem, called cost-aware targeted viral marketing (CTVM), to find the most costeffective seed users, who can influence the most relevant users to the advertisement. Since CTVM is NP-hard, we design an efficient (1 - 1/√e - ∈)-approximation algorithm, named Billion-scale Cost-award Targeted algorithm (BCT), to solve the problem in billion-scale networks. Comparing with IM algorithms, we show that BCT is both theoretically and experimentally faster than the state-of-the-arts while providing better solution quality. Moreover, we prove that under the linear threshold model, BCT is the first sub-linear time algorithm for CTVM (and IM) in dense networks. We carry a comprehensive set of experiments on various real-networks with sizes up to several billion edges in diverse disciplines to show the absolute superiority of BCT on both CTVM and IM domains. Experiments on Twitter data set, containing 1.46 billions of social relations and 106 millions tweets, show that BCT can identify key influencers in trending topics in only few minutes.

Device-to-device (D2D) communications over the licensed wireless spectrum has been recently proposed as a promising technology to meet the capacity crunch of next generation cellular networks. However, due to the high mobility of cellular devices, establishing and ensuring the success of D2D transmission become a major challenge. To this end, in this paper, a novel framework is proposed to enable devices to form multi-hop D2D connections in an effort to maintain sustainable communication in the presence of device mobility. To solve the problem posed by device mobility, in contrast to existing works, which mostly focus on physical domain information, a durable community-based approach is introduced taking social encounters into context. It is shown that the proposed scheme can derive an optimal solution for time sensitive content transmission while also minimizing the cost that the base station pays in order to incentivize users to participate in D2D. Simulation results show that the proposed social community aware approach yields significant performance gain, in terms of the amount of traffic offloaded from the cellular network to the D2D tier, compared with the classical social-unaware methods.

While online social networks provide access to a massive information source, they also enable wide dissemination of false or inaccurate content. Undesirable results caused by misinformation propagation make its timely detection very imperative. An important question is how many monitors are required to detect all misinformation cascades at their early stage. To answer this question, we define a Time Constrained Misinformation Detection (TCMD) problem. As we have proved, there is no polynomial time (1 - ε) ln n-approximation for the TCMD problem. The large number of independent misinformation cascades and heterogeneous delays make misinformation detection more challenging. Our approach includes stochastic programming and an O(ln(1 + n)) approximation algorithm for one-hop detection. This approach can provide a lower bound on the number of required monitors for general detection. Furthermore, we propose a network-compression based solution, whose effectiveness is validated by extensive experimental results.

Online social networks have been one of the most effective platforms for marketing and advertising. Through the “world-of-mouth” exchanges, so-called viral marketing, the influence and product adoption can spread from few key influencers to billions of users in the network. To identify those key influencers, a great amount of work has been devoted for the Influence Maximization (IM) problem that seeks a set of k seed users that maximize the expected influence. Unfortunately, IM encloses two impractical assumptions: 1) any seed user can be acquired with the same cost and 2) all users are equally interested in the advertisement. In this paper, we propose a new problem, called Cost-aware Targeted Viral Marketing (CTVM), to find the most cost-effective seed users who can influence the most relevant users to the advertisement. Since CTVM is NP-hard, we design an efficient (1 − 1/√e-∊ − e)-approximation algorithm, named BCT, to solve the problem in billion-scale networks. Comparing with IM algorithms, we show that BCT is both theoretically and experimentally faster than the state-of-the-arts while providing better solution quality. Moreover, we prove that under the Linear Threshold model, BCT is the first sub-linear time algorithm for CTVM (and IM) in dense networks. In our experiments with a Twitter dataset, containing 1.46 billions of social relations and 106 millions tweets, BCT can identify key influencers in each trending topic in only few minutes.

Information propagation in online social networks (OSNs), which helps shaping consumers' purchasing decisions, has received a lot of attention. The ultimate goal of marketing and advertising in OSNs is to massively influence audiences and enlarge the number of product adoptions. Most of existing works focus on maximizing the influence of a single product or promoting the adoption of one product in competing campaigns. However, in reality, the majority of companies produce various products for supplying customers with different needs. Therefore, it is truly significant and also challenging to wisely distribute limited budget across multiple products in viral marketing. In this paper, we investigate a Profit Maximization with Multiple Adoptions (PM2A) problem, which aims at maximizing the overall profit across all products. The natural greedy fails to provide a bounded result. In order to select high quality seeds for information propagation, we first proposed the PMCE algorithm, which has a ratio 1/2 (1 - 1/e2). Moreover, we further improve this ratio to (1-1/e) by proposing the PMIS algorithm. Comprehensive experiments on three real social networks are conducted. And results show that our algorithms outperform other heuristics, and better distribute the budget in terms of profit maximization.

Influence Maximization (IM), that seeks a small set of key users who spread the influence widely into the network, is a core problem in multiple domains. It finds applications in viral marketing, epidemic control, and assessing cascading failures within complex systems. Despite the huge amount of effort, IM in billion-scale networks such as Facebook, Twitter, and World Wide Web has not been satisfactorily solved. Even the state-of-the-art methods such as TIM+ and IMM may take days on those networks. In this paper, we propose SSA and D-SSA, two novel sampling frameworks for IM-based viral marketing problems. SSA and D-SSA are up to 1200 times faster than the SIGMOD'15 best method, IMM, while providing the same (1-1/e-ε) approximation guarantee. Underlying our frameworks is an innovative Stop-and-Stare strategy in which they stop at exponential check points to verify (stare) if there is adequate statistical evidence on the solution quality. Theoretically, we prove that SSA and D-SSA are the first approximation algorithms that use (asymptotically) minimum numbers of samples, meeting strict theoretical thresholds characterized for IM. The absolute superiority of SSA and D-SSA are confirmed through extensive experiments on real network data for IM and another topic-aware viral marketing problem, named TVM.

A reconnaissance attack, in which attackers lure targets into becoming their friends in order to extract victims' sensitive information for sale or use in future attacks, is one of the most dangerous attacks in social networks. The core of this attack lies in intelligently sending friend requests to a small subset of users, called Critical Friending Set (CFS), so that the attacker can evade current defense mechanisms. Motivated by the above, we present a new paradigm to measure OSN vulnerability in light of reconnaissance attacks. Specifically, we introduce a new optimization problem, namely Min-Friending, which identifies a minimum CFS to friend in order to obtain at least Q benefit, in terms of personal information. A significant challenge of this problem is that network information (i.e. who friends with whom) is generally unknown to attackers. In this paper, we show that Min-Friending is inapproximable within a factor of (1 - o(1)) ln Q and present an adaptive algorithm which has a tight performance bound of (1+ln Q) using adaptive stochastic optimization. The key feature of our solution lies in the adaptive method, where partial network topology is revealed during each successful friend request. Thus the decision of sending each friend request is made taking into account observation about the outcomes of past decisions.

Opportunistic networks enable mobile devices to communicate with each other through routes that are built dynamically, while messages are en route between the sender and the destination(s). The social structure and interaction of users of such devices dictate the performance of routing protocols in those networks. Community structures, commonly exhibited by social networks, is also observed in the encounter patterns in opportunistic networks and has an astounding impact in designing forwarding algorithms for such types of networks. In this paper, we explore the structural vulnerability of social-based forwarding and routing methods in opportunistic networks. In particular, we introduce Community Vulnerability Assessment (CVA), a new problem on assessing the performance reliability of opportunistic routing strategies in Delay Tolerant Networks (DTN) from a community structure point of view. Given a positive number k, CVA aims to find out the k most vulnerable devices in the network whose non-participation (due to out-of-service or permanent out-of-range) transforms the current network community structure to a totally different one. As the first study in this direction, we analyze and provide key insights into the separation of network communities, evaluated via the Normalized Mutual Information (NMI). Based on these findings, we suggest an approximation algorithm for the special case when k 1/4 1, and a heuristic, genEdge, for the general case. To certify the effectiveness of our proposed approaches, we first test them on synthesized data with known community structures, and then we show the impact of node removal on community structures in real social networks. Finally we evaluate the performance via different forwarding and routing strategies in multiple real-world DTN traces. Our results indicate that, in many forwarding and routing methods, the nonparticipation of only some important devices is significant enough to degrade the entire network's performance.

Online social networks have become an effective and important social platform for communication, opinions exchange, and information sharing. However, they also make it possible for rapid and wide misinformation diffusion, which may lead to pernicious influences on individuals or society. Hence, it is extremely important and necessary to detect the misinformation propagation by placing monitors. In this article, we first define a general misinformation-detection problem for the case where the knowledge about misinformation sources is lacking, and show its equivalence to the influence-maximization problem in the reverse graph. Furthermore, considering node vulnerability, we aim to detect the misinformation reaching to a specific user. Therefore, we study a τ-Monitor Placement problem for cases where partial knowledge of misinformation sources is available and prove its # P complexity. We formulate a corresponding integer program, tackle exponential constraints, and propose a Minimum Monitor Set Construction (MMSC) algorithm, in which the cut-set 2 has been exploited in the estimation of reachability of node pairs. Moreover, we generalize the problem from a single target to multiple central nodes and propose another algorithm based on a Monte Carlo sampling technique. Extensive experiments on real-world networks show the effectiveness of proposed algorithms with respect to minimizing the number of monitors.

In the above paper[1], one author was inadvertently left out of the byline of the paper. The byline should have read as follows:

Recently, in online social networks (OSNs), the least cost influence (LCI) problem has become one of the central research topics. It aims at identifying a minimum number of seed users who can trigger a wide cascade of information propagation. Most of existing literature investigated the LCI problem only based on an individual network. However, nowadays users often join several OSNs such that information could be spread across different networks simultaneously. Therefore, in order to obtain the best set of seed users, it is crucial to consider the role of overlapping users under this circumstances. In this article, we propose a unified framework to represent and analyze the influence diffusion in multiplex networks. More specifically, we tackle the LCI problem by mapping a set of networks into a single one via lossless and lossy coupling schemes. The lossless coupling scheme preserves all properties of original networks to achieve high-quality solutions, while the lossy coupling scheme offers an attractive alternative when the running time and memory consumption are of primary concern. Various experiments conducted on both real and synthesized datasets have validated the effectiveness of the coupling schemes, which also provide some interesting insights into the process of influence propagation in multiplex networks.

Online Social Networks (OSNs), such as Facebook, Twitter and Google+, facilitate the interactions and communications among people. However, they also make it a fertile land for misinformation to rapidly spread out, which may lead to detrimental consequences. Thus it is imperative to detect the misinformation propagating through OSNs by placing monitors. In this paper, we first study a general misinformation detection problem and show its equivalence to the influence maximization problem. Moreover, in order to prevent misinformation from reaching specific users, we define a τ-Monitor Placement problem for cases where the partial knowledge of misinformation sources is available. We prove the #P complexity of this problem and additionally propose an efficient algorithm to solve it. Extensive experiments on real-world data show the effectiveness of our proposed algorithm with respect to minimizing the number of monitors.

Many social networks and complex systems are found to be naturally divided into clusters of densely connected nodes, known as community structure (CS). Finding CS is one of fundamental yet challenging topics in network science. One of the most popular classes of methods for this problem is to maximize Newman's modularity. However, there is a little understood on how well we can approximate the maximum modularity as well as the implications of finding community structure with provable guarantees. In this paper, we settle definitely the approximability of modularity clustering, proving that approximating the problem within any (multiplicative) positive factor is intractable, unless P = NP. Yet we propose the first additive approximation algorithm for modularity clustering with a constant factor. Moreover, we provide a rigorous proof that a CS with modularity arbitrary close to maximum modularity QOPT might bear no similarity to the optimal CS of maximum modularity. Thus even when CS with near-optimal modularity are found, other verification methods are needed to confirm the significance of the structure.

A considerable amount of research effort has focused on developing metrics and approaches to assess network vulnerability. However, most of them neglect the network uncertainty arisen due to various reasons such as mobility and dynamics of the network, or noise introduced in data collection process. To this end, we introduce a framework to assess vulnerability of networks with uncertainty, modeling such networks as probabilistic graphs. We adopt expected pairwise connectivity (EPC) as a measure to quantify global connectivity and use it to formulate vulnerability assessment as a stochastic optimization problem. The objective is to identify a few number of critical nodes whose removal minimizes EPC in the residual network. While solutions for stochastic optimization problems are often limited to small networks, we present a practical solution that works for larger networks. The key advantages of our solution include 1) the application of a weighted averaging technique that avoids considering all, exponentially many, possible realizations of probabilistic graphs and 2) a Fully Polynomial Time Randomized Approximation Scheme (FPRAS) to efficiently estimate the EPC with any desired accuracy. Extensive experiments demonstrate significant improvement on performance of our solution over other heuristic approaches.

Smart Grid addresses the problem of existing power grid's increasing complexity, growing demand and requirement for greater reliability, through two-way communication and automated residential load control among others. These features also makes the Smart Grid a target for a number of cyber attacks. In the paper, we study the problem of rate alteration attack (RAA) through fabrication of price messages which induces changes in load profiles of individual users and eventually causes major alteration in the load profile of the entire network. Combining with cascading failure, it ends up with a highly damaging attack. We prove that the problem is NP-Complete and provide its inapproximability. We devise two approaches for the problem, former deals with maximizing failure of lines with the given resource and then extending the effect with cascading failure while the later takes cascading potential into account while choosing the lines to fail. To get more insight into the impact of RAA, we also extend our algorithms to maximize number of node failures. Empirical results on both IEEE Bus data and real network help us evaluate our approaches under various settings of grid parameters.

Critical infrastructures such as communication networks, electrical grids, and transportation systems are highly vulnerable to natural disasters and malicious attacks. Even failures of few nodes or links may have a profound impact on large parts of the system. Traditionally, network vulnerability assessment methods separate the studies of node vulnerability and link vulnerability, and thus ignore joint node and link attack schemes that may cause grave damage to the network. To this end, we introduce a new assessment method, called β-disruptor, that unifies both link and node vulnerability assessment. The new assessment method is formulated as an optimization problem in which we aim to identify a minimum-cost set of mixed links and nodes whose removal would severely disrupt the network connectivity. We prove the NP-completeness of the problem and propose an O(√{logn}) bicriteria approximation algorithm for the β-disruptor problem. This new theoretical guarantee improves the best approximation results for both link and node vulnerability assessment in literature. We further enhance the proposed algorithm by embedding it into a special combination of simulated annealing and variable neighborhood search method. The results of our extensive simulation-based experiments on synthetic and real networks show the feasibility and efficiency of our proposed vulnerability assessment methods.

Many complex systems, from World Wide Web and online social networks to mobile networks, exhibit community structure in which nodes can be grouped into densely interconnected communities. This special structure has been exploited extensively to design better solutions for many operations and applications such as routing in wireless networks, worm containment and interest prediction in social networks. The outcome of these solutions are sensitive to the network structures, which raises an important question: can communities be broken easily in a network? To answer this question, we introduce a density-based problem formulation for analyzing the vulnerability of communities. Our approach includes the NP-completeness and a O(log k) approximation algorithm for solving the problem where k is the number of communities to be broken. Additionally, we analyze the vulnerability of communities in the context of arbitrary community detection algorithms. The empirical results show that communities are vulnerable to edge removal and in some cases the removal of a small fraction of edges can break the community structure.

Device-to-device (D2D) communications has recently emerged as a promising technology for boosting the capacity of cellular systems. D2D enables direct communication between mobile devices over the cellular band without utilizing infrastructure nodes such as base stations, thereby reducing the load on cellular base stations and increasing network throughput through spatial reuse of radio resources. Hence it is important to optimally allocate these radio resources. Furthermore, since the composition of a cellular macro cell is highly dynamic, it is critical to adaptively update the resource allocation for D2D communications rather than recomputing it from scratch. In this work, we develop the first online algorithm, namely ODSRA, for dynamic resource allocation while maximizing spatial reuse. At the core of the resource allocation problem is the online set multicover problem, for which we present the first deterministic O (log n log m)-competitive online algorithm, where n is the number of elements, and m the number of sets. By simulation, we show the efficacy of ODSRA by analyzing network throughput and other metrics, obtaining a large improvement in running time over offline methods.

Online social networks (OSNs) have become one of the most effective channels for marketing and advertising. Since users are often influenced by their friends, “word-of-mouth” exchanges, so-called viral marketing, in social networks can be used to increase product adoption or widely spread content over the network. The common perception of viral marketing about being cheap, easy, and massively effective makes it an ideal replacement of traditional advertising. However, recent studies have revealed that the propagation often fades quickly within only few hops from the sources, counteracting the assumption on the self-perpetuating of influence considered in literature. With only limited influence propagation, is massively reaching customers via viral marketing still affordable? How do we economically spend more resources to increase the spreading speed? We investigate the cost-effective massive viral marketing problem, taking into the consideration the limited influence propagation. Both analytical analysis based on power-law network theory and numerical analysis demonstrate that the viral marketing might involve costly seeding. To minimize the seeding cost, we provide mathematical programming to find optimal seeding for medium-size networks and propose VirAds, an efficient algorithm, to tackle the problem on large-scale networks. VirAds guarantees a relative error bound of O(1) from the optimal solutions in power-law networks and outperforms the greedy heuristics that realizes on the degree centrality. Moreover, we also show that, in general, approximating the optimal seeding within a ratio better than O(logn) is unlikely possible.

Phenomena clouds are characterized by nondeterministic, dynamic variations of shapes, sizes, direction, and speed of motion along multiple axes. The phenomena detection and tracking should not be limited to some traditional applications such as oil spills and gas clouds but also be utilized to more accurately observe other types of phenomena such as walking motion of people. This wider range of applications requires more reliable, in-situ techniques that can accurately adapt to the dynamics of phenomena. Unfortunately, existing works which only focus on simple and well-defined shapes of phenomena are no longer sufficient. In this article, we present a new class of applications together with several distributed algorithms to detect and track phenomena clouds, regardless of their shapes and movement direction. We first propose a distributed algorithm for in-situ detection and tracking of phenomena clouds in a sensor space. We next provide a mathematical model to optimize the energy consumption, on which we further propose a localized algorithm to minimize the resource utilization. Our proposed approaches not only ensure low processing and networking overhead at the centralized query processor but also minimize the number of sensors which are actively involved in the detection and tracking processes. We validate our approach using both real-life smart home applications and simulation experiments, which confirm the effectiveness of our proposed algorithms. We also show that our algorithms result in significant reduction in resource usage and power consumption as compared to contemporary stream-based approaches.

We introduce Community structure Vulnerability Assessment (CVA) problem to assess the network vulnerability under a community structure point of view. Given a positive number k, CVA aims to find out the k most vulnerable nodes whose removals maximally transform the current network community structure to a different one. As the first attempt, we suggest an approximation algorithm for the special case k = 1, and propose multiple greedy algorithms for CVA problem. To certify the effectiveness of suggested approaches, we test them on not only synthesized networks with known community structures but also on real-world social traces.

We study the influence maximization problem (IMP) in multiple online social networks (OSNs). In contrast to most of the previous works on influence maximization which only focused on a single network, our work is the first one to evaluate the propagation of influence across multiple networks simultaneously. In this paper, we first propose a general representation for multiple networks using universal ids for the users. We next introduce a powerful coupling scheme which reduces the multiple networks into a single network without changing influencing properties, thereby allowing us to solve the problem in the reduced network. Moreover, the coupling scheme is also an efficient tool to investigate various aspects of the influence propagation on multiple OSNs. The extensive experiments on real-world and synthesized datasets not only confirm the quality of the solution but also reveal interesting insights into the behavior of influence propagation in and across the networks.

The assessment of network vulnerability is of great importance in the presence of unexpected disruptive events or adversarial attacks, which will lead to a much more devastating consequence especially when failures can be cascaded. In this context, we study the Cascading Vulnerability Node Detection (CVND) optimization problem to identify the most vulnerable nodes in a network whose removals maximally destroy the network's functions after cascading failures, based on the recently proposed effective metric, total pairwise connectivity. Besides its NP-hardness on various graphs, we further show that the CVND problem is NP-hard to be approximated within Ω ((1+(d/n1-∈-1)2(n-k)/n∈)) equation with n vertices and k vulnerable nodes after d-hop failure cascades. Despite the intractability of this problem, we propose TRGA, a novel iterative two-phase algorithm, for efficiently solving the CVND problem in a timely manner. We also formulate the integer linear programming for obtaining the optimal solution. The effectiveness of our solutions is validated on various synthetic and real-world networks.

Online social networks (OSNs) provide a new platform for product promotion and advertisement. Influence maximization problem arisen in viral marketing has received a lot of attentions recently. Most of the existing diffusion models rely on one fundamental assumption that an influenced user necessarily adopts the product and encourages his/her friends to further adopt it. However, an influenced user may be just aware of the product. Due to personal preference, neutral or negative opinion can be generated so that product adoption is uncertain. Maximizing the total number of influenced users is not the uppermost concern, instead, letting more activated users hold positive opinions is of first importance. Motivated by above phenomenon, we proposed a model, called Opinion-based Cascading (OC) model. We formulate an opinion maximization problem on the new model to take individual opinion into consideration as well as capture the change of opinions at the same time. We show that under the OC model, opinion maximization is NP-hard and the objective function is no longer submodular. We further prove that there does not exist any approximation algorithm with finite ratio unless P=NP. We have designed an efficient algorithm to compute the total positive influence based on this new model. Comprehensive experiments on real social networks are conducted, and results show that previous methods overestimate the overall positive influence, while our model is able to distinguish between negative opinions and positive opinions, and estimate the overall influence more accurately.

The least cost influence (LCI) problem, which asks to identify a minimum number of seed users who can eventually influence a large number of users, has become one of the central research topics recently in online social networks (OSNs). However, existing works mostly focused on a single network while users nowadays often join several OSNs. Thus, it is crucial to investigate the influence in multiplex networks, i.e. the influence is diffused across a set of networks via shared users, in order to obtain the best set of seed users.In this paper, we propose a unified framework to represent and analyze the influence diffusion in multiplex networks. More specifically, we tackle the LCI problem in multiplex OSNs by reducing multiplex networks to a single network via various coupling schemes while preserving the most influence propagation properties. Besides the coupling schemes to represent the diffusion process, the framework also includes the influence relay, a new metric to measure the flow of influence inside and between networks. The experiments on both real and synthesized datasets validate the effectiveness of the coupling schemes as well as provide some interesting insights into the process of influence propagation in multiplex networks.

We introduce A3CS, an adaptive framework with approximation guarantees for quickly identifying community structure in dynamic networks via maximizing Modularity Q. Our framework explores the advantages of power-law distribution property, is scalable for very large networks, and more excitingly, possesses approximation factors to ensure the quality of its detected community structure. To the best of our knowledge, this is the first framework that achieves approximation guarantees for the NP-hard modularity maximization problem, especially on dynamic networks. To certify our approach, we conduct extensive experiments in comparison with other adaptive methods on both synthesized networks with known community structures and real-world traces including ArXiv e-print citation and Facebook social networks. Excellent empirical results not only confirm our theoretical results but also promise the practical applicability of A3CS in a wide range of dynamic networks.

Region coverage and network connectivity are among the most important problems for the quality of service in wireless sensor networks. Unfortunately, due to the sensor failures and hostile environments, such as active volcanic regions or battle fields, the emergence of coverage holes and disconnections among sensors is unavoidable. One way to handle this problem is to deploy mobile sensors in the network, which is called hybrid sensor networks, so that these mobile sensors can be relocated to heal the holes or maintain the network connectivity. However, because of the low-power of mobile sensors, it is extremely challenging to design a fast and effective movement schedule for mobile sensors to (1) maintain both the region coverage and network connectivity at any time, and (2) minimize the moving energy consumption. In this paper, we develop an adaptive algorithm, AHCH algorithm, to adaptively heal the holes with the guarantee of network connectivity without recomputing from scratch. By comparing AHCH algorithm with the optimal solution at each time-slot, we show its expected adaptive approximation ratio as O(log |M|) with |M| mobile sensors in some special cases. In more general cases, we extend our AHCH algorithm to InAHCH and GenAHCH algorithms, handling insufficient mobile sensors as well as disconnected regions, along with the proof of their corresponding theoretical adaptive approximation ratios. The experimental evaluation shows the effectiveness of our proposed algorithms with respect to both low energy consumption and hole healing latency.

Many networks, indifferent of their function and scope, converge to a scale-free architecture in which the degree distribution approximately follows a power law. Meanwhile, many of those scale-free networks are found to be naturally divided into communities of densely connected nodes, known as community structure. Finding this community structure is a fundamental but challenging topic in network science. Since Newman's suggestion of using modularity as a measure to qualify the strength of community structure, many efficient methods that find community structure based on maximizing modularity have been proposed. However, there is a lack of approximation algorithms that provide provable quality bounds for the problem. In this paper, we propose polynomial-time approximation algorithms for the modularity maximization problem together with their theoretical justifications in the context of scale-free networks. We prove that the solutions of the proposed algorithms, even in the worst-case, are optimal up to a constant factor for scale-free networks with either bidirectional or unidirectional links. Even though our focus in this work is not on designing another empirically good algorithms to detect community structure, experiments on real-world networks suggest that the proposed algorithm is competitive with the state-of-the-art modularity maximization algorithm.

Link failure localization has been an important and challenging problem for all-optical networks. The most general monitoring structure, called m-trail, is a light-path into which optical signals are launched and monitored. How to minimize the number of required m-trails is critical to the expense of this technique. Existing solutions are limited to localizing single link failure or handling only small networks. Moreover, some practical constraints, like lacking of knowledge of the failure quantity, are ignored. To overcome these limitations is prospective but quite challenging. To this end, we provide novel theoretical solution frameworks toward the multi-failure localization problem. On one hand, for small dense networks, we provide a tree-decomposition based algorithm; on the other hand, a random walk based localized algorithm for large scale sparse networks is proposed. In addition, we further adapt these two algorithms to cope with three practical constraints. Theoretical analysis and simulation results are included to prove the correctness and efficiency of the proposed schemes.

Broadcast scheduling in multihop Wireless Sensor Networks (WSNs) is an effective mechanism to perform interference-aware broadcasting. Existing works provide centralized solutions, which cannot be implemented locally. Additionally, they consider very elementary network and interference models, in which, either all sensor nodes have the same transmission range or their transmission ranges are equal to their interference ranges that are not very practical. Furthermore, they entirely ignore the existence of WSNs in 3D. In this paper, we study the broadcast scheduling in 2D and 3D WSNs. We consider that sensor nodes may have different transmission ranges and their interference ranges are α times of their transmission ranges (where α >; 1). We devise efficient coloring methods for coloring a hexagonal tiling in 2D plane and a truncated octahedron tiling in 3D space, based on which we propose O(1)-centralized approximation algorithms and O(1)-localized approximation algorithms for the broadcast scheduling problem in 2D and 3D WSNs, respectively. Our O(1)-centralized approximation algorithms for 3D WSNs and O(1)-localized approximation algorithms for 2D and 3D WSNs are the first approximation algorithms for the corresponding problems. Finally, we present an efficient greedy heuristic to study the effect of various priority metrics for greedily scheduling multiple interfering transmissions. Theoretical analysis and experimental results are provided to evaluate the performance of our algorithms.

The assessment of network vulnerability is of great importance in the presence of unexpected disruptive events or adversarial attacks targeting on critical network links and nodes. In this paper, we study Critical Link Disruptor (CLD) and Critical Node Disruptor (CND) optimization problems to identify critical links and nodes in a network whose removals maximally destroy the network's functions. We provide a comprehensive complexity analysis of CLD and CND on general graphs and show that they still remain NP-complete even on unit disk graphs and power-law graphs. Furthermore, the CND problem is shown NP-hard to be approximated within Ω([(n-k)/(nε)] ) on general graphs withnvertices andkcritical nodes. Despite the intractability of these problems, we propose HILPR, a novel LP-based rounding algorithm, for efficiently solving CLD and CND problems in a timely manner. The effectiveness of our solutions is validated on various synthetic and real-world networks.

With a rapid expansion of online social networks (OSNs), millions of users are tweeting and sharing their personal status daily without being aware of where that information eventually travels to. Likewise, with a huge magnitude of data available on OSNs, it poses a substantial challenge to track how a piece of information leaks to specific targets. In this paper, we study the problem of smartly sharing information to control the propagation of sensitive information in OSNs.

Online social networks have become an imperative channel for extremely fast information propagation and influence. Thus, the problem of finding a minimum number of seed users who can eventually influence as many users in the network as possible has become one of the central research topics recently. Unfortunately, most of related works have only focused on the network topologies and largely ignored many other important factors such as the users' engagements and the negative or positive impacts between users. More challengingly, the behavior of information propagation across multiple networks simultaneously remains an untrodden area and becomes an urgent need. Our work is the first attempt to tackle the above problem in multiple networks, considering these lacking important factors. In order to capture the users' engagement, we propose to targeting the set of interest-matching users whose interests are similar to what we try to propagate. Then, we develop our Iterative Semi-Supervising Learning based approach to identify the minimum seed users. We validate the effectiveness of our solution by using real-world Twitter-Foursquare networks and academic collaboration multiple networks.

During the last decade, Reactive Jamming Attack has emerged as a great security threat to wireless sensor networks, due to its mass destruction to legitimate sensor communications and difficulty to be disclosed and defended. Considering the specific characteristics of reactive jammer nodes, a new scheme to deactivate them by efficiently identifying all trigger nodes, whose transmissions invoke the jammer nodes, has been proposed and developed. Such a trigger-identification procedure can work as an application-layer service and benefit many existing reactive-jamming defending schemes. In this paper, on the one hand, we leverage several optimization problems to provide a complete trigger-identification service framework for unreliable wireless sensor networks. On the other hand, we provide an improved algorithm with regard to two sophisticated jamming models, in order to enhance its robustness for various network scenarios. Theoretical analysis and simulation results are included to validate the performance of this framework.

Society relies heavily on its networked physical infrastructure and information systems. Accurately assessing the vulnerability of these systems against disruptive events is vital for planning and risk management. Existing approaches to vulnerability assessments of large-scale systems mainly focus on investigating inhomogeneous properties of the underlying graph elements. These measures and the associated heuristic solutions are limited in evaluating the vulnerability of large-scale network topologies. Furthermore, these approaches often fail to provide performance guarantees of the proposed solutions. In this paper, we propose a vulnerability measure, pairwise connectivity, and use it to formulate network vulnerability assessment as a graph-theoretical optimization problem, referred to as -disruptor. The objective is to identify the minimum set of critical network elements, namely nodes and edges, whose removal results in a specific degradation of the network global pairwise connectivity. We prove the NP-completeness and inapproximability of this problem and propose an pseudo-approximation algorithm to computing the set of critical nodes and an pseudo-approximation algorithm for computing the set of critical edges. The results of an extensive simulation-based experiment show the feasibility of our proposed vulnerability assessment framework and the efficiency of the proposed approximation algorithms in comparison to other approaches.

Social networks exhibit a very special property: community structure. Understanding the network community structure is of great advantages. It not only provides helpful information in developing more social-aware strategies for social network problems but also promises a wide range of applications enabled by mobile networking, such as routings in Mobile Ad Hoc Networks (MANETs) and worm containments in cellular networks. Unfortunately, understanding this structure is very challenging, especially in dynamic social networks where social activities and interactions are evolving rapidly. Can we quickly and efficiently identify the network community structure? Can we adaptively update the network structure based on previously known information instead of recomputing from scratch? In this paper, we present Quick Community Adaptation (QCA), an adaptive modularity-based method for identifying and tracing community structure of dynamic online social networks. Our approach has not only the power of quickly and efficiently updating network communities, through a series of changes, by only using the structures identified from previous network snapshots, but also the ability of tracing the evolution of community structure over time. To illustrate the effectiveness of our algorithm, we extensively test QCA on real-world dynamic social networks including ENRON email network, arXiv e-print citation network and Facebook network. Finally, we demonstrate the bright applicability of our algorithm via a realistic application on routing strategies in MANETs. The comparative results reveal that social-aware routing strategies employing QCA as a community detection core outperform current available methods.

Network coverage is one of the most decisive factors for determining the efficiency of a wireless sensor network. However, in dangerous or hostile environments such as battle fields or active volcano areas, we can neither deterministically or purposely deploy sensors as desired, thus the emergence of coverage holes (the unmonitored areas) is unavoidable. In addition, the introduction of new coverage holes during network operation due to sensor failures due to energy depletion shall significantly reduce coverage efficacy. Therefore, we need to either remotely control or set up a protocol to heal them as soon as possible in an automated fashion. In this paper, we focus on how to schedule mobile sensors in order to cope with coverage hole issues in a hybrid sensor network containing both static and mobile sensors. To this end, we introduce a new metric, namely to maximize the minimum remaining energy of all moved sensor since the more energy remains, the longer the network can operate. Based on this metric, we propose an efficient coverage healing algorithm that always determines an optimal location for each mobile sensor in order to heal all coverage holes, after all mobile sensors locations and coverage holes are located. Simulation results confirm the efficiency and utilization of our proposed method.

Many practical problems on Mobile networking, such as routing strategies in MANETs, sensor reprogramming in WSNs and worm containment in online social networks (OSNs) share an ubiquitous, yet interesting feature in their organizations: community structure. Knowledge of this structure provides us not only crucial information about the network principles, but also key insights into designing more effective algorithms for practical problems enabled by Mobile networking. However, understanding this interesting feature is extremely challenging on dynamic networks where changes to their topologies are frequently introduced, and especially when network communities in reality usually overlap with each other. We focus on the following questions (1) Can we effectively detect the overlapping community structure in a dynamic network? (2) Can we quickly and adaptively update the network structure only based on its history without recomputing from scratch? (3) How does the detection of network communities help mobile applications? We propose AFOCS, a two-phase framework for not only detecting quickly but also tracing effectively the evolution of overlapped network communities in dynamic mobile networks. With the great advantages of the overlapping community structure, AFOCS significantly helps in reducing up to 7 times the infection rates in worm containment on OSNs, and up to 11 times overhead while maintaining good delivery time and ratio in forwarding strategies in MANETs.

Unlike traditional distributed systems, where the resources/needs of computation and communication dominate the performance equation, sensor-based systems (SBS) raise new metrics and requirements for sensors as well as for computing and communication. This includes sensing latency and energy consumption. In this paper, we present a performance model for SBS based on a three-tier architecture that uses edge devices to connect massive-scale networks of sensors to the cloud. In this architecture, which we call Cloud, Edge, and Beneath (CEB), initial processing of sensor data occurs in- and near-network, in order to achieve system sentience and energy efficiency. To optimize CEB performance, we propose the concept of optimal push/pull envelope (PPE). PPE dynamically and minimally adjusts the base push and pull rates for each sensor, according to the relative characteristics of sensor requests (demand side from the Cloud) and sensor data change (supply side from Beneath). We demonstrate the CEB architecture and its push/pull envelope optimization algorithm in an experimental evaluation that measures energy savings and sentience efficiency over a wide range of practical constraints. In addition, from the experiments we demonstrate that by combining PPE optimization algorithm with lazy sampling algorithm, we can achieve further energy saving.

Often groups of genes in regulatory networks, also called modules, work collaboratively on similar functions. Mathematically, the modules in a regulatory network has often been thought as a group of genes that interact with each other significantly more than the rest of the network. Finding such modules is one of the fundamental problems in understanding gene regulation. In this paper, we develop a new approach to identify modules of genes with similar functions in biological regulatory networks (BRNs). Unlike existing methods, our method recognizes that there are different types of interactions (activation, inhibition), these interactions have directions and they take place only if the activity levels of the activating (or inhibiting) genes are above certain thresholds. Furthermore, it also considers that as a result of these interactions, the activity levels of the genes change over time even in the absence of external perturbations. Here we addresses both the dynamic behavior of gene activity levels and the different interaction types by an incremental algorithm that is scalable to the organism wide BRNs with many dynamic steps. Our experimental results suggest that our method can identify biologically meaningful modules that are missed by traditional approaches.

Dynamic phenomena such as oil spills, mud flow, diffusion or leakage of gases in the environment are characterized by non-deterministic variations in shape, size and direction of motion. Due to the absence of any well defined model for tracking their dynamics, the detection and tracking of such phenomena through Wireless Sensor Networks (WSNs) is very challenging. Most of the existing works consider static phenomena with certain shapes, only few of them study dynamic phenomena. However, existing works studying dynamic phenomena mainly concentrate on reducing the communication overheads and neglect the energy consumed in sensing, which result in higher energy consumption and shorter network lifetime. In this paper, we propose a novel protocol for detection and tracking dynamic phenomena through WSNs, with an objective to minimize the resource usage and energy consumption. We also propose a robust localized clustering method, which helps in reducing the network traffic generated by the information packets destined to a Centralized Query Processor (CQP). The experimental results show that, in comparison to the existing work, our protocol consumes 90% less energy and generates 65% less network traffic.

How to assess the topology vulnerability of a network has attracted more and more attentions recently. Due to the rapid growing number of real- time internet applications developed since the last decade, the discovery of topology weakness related to its quality of service (QoS) is of more interest. In this paper, we provide a novel QoS-aware measurement for assessing the vulnerability of general network topologies. Specifically, we evaluate the vulnerability by detecting the minimum number of link failures that decrease the satisfactory level of the QoS-Optimal source- destination path to a given value, which means a topology with a smaller amount of such link failures is more vulnerable. We formulate this process as a graph optimization problem called QoSCE and provide several exact and heuristic algorithms for various QoS constraint amounts. To our best knowledge, this is the first graph-theoretical framework to evaluate QoS-aware topology vulnerability. Through extensive simulations, the performance of the proposed algorithms are validated in terms of assessment accuracy and time complexity.

Assessing network vulnerability before potential disruptive events such as natural disasters or malicious attacks is vital for network planning and risk management. It enables us to seek and safeguard against most destructive scenarios in which the overall network connectivity falls dramatically. Existing vulnerability assessments mainly focus on investigating the inhomogeneous properties of graph elements, node degree for example, however, these measures and the corresponding heuristic solutions can provide neither an accurate evaluation over general network topologies, nor performance guarantees to large scale networks. To this end, in this paper, we investigate a measure called pairwise connectivity and formulate this vulnerability assessment problem as a new graph-theoretical optimization problem called ß-disruptor, which aims to discover the set of critical node/edges, whose removal results in the maximum decline of the global pairwise connectivity. Our results consist of the NP-Completeness and inapproximability proof of this problem, an O(log n log log n) pseudo-approximation algorithm for detecting the set of critical nodes and an O(log1.5n) pseudo-approximation algorithm for detecting the set of critical edges. In addition, we devise an efficient heuristic algorithm and validate the performance of the our model and algorithms through extensive simulations.

Application DoS attack, which aims at disrupting application service rather than depleting the network resource, has emerged as a larger threat to network services, compared to the classic DoS attack. Owing to its high similarity to legitimate traffic and much lower launching overhead than classic DDoS attack, this new assault type cannot be efficiently detected or prevented by existing detection solutions. To identify application DoS attack, we propose a novel group testing (GT)-based approach deployed on back-end servers, which not only offers a theoretical method to obtain short detection delay and low false positive/negative rate, but also provides an underlying framework against general network attacks. More specifically, we first extend classic GT model with size constraints for practice purposes, then redistribute the client service requests to multiple virtual servers embedded within each back-end server machine, according to specific testing matrices. Based on this framework, we propose a two-mode detection mechanism using some dynamic thresholds to efficiently identify the attackers. The focus of this work lies in the detection algorithms proposed and the corresponding theoretical complexity analysis. We also provide preliminary simulation results regarding the efficiency and practicability of this new scheme. Further discussions over implementation issues and performance enhancements are also appended to show its great potentials.

Many communication networks such as Mobile Ad Hoc Networks (MANETs) involve in human interactions and exhibit properties of social networks. Hence, it is interesting to see how knowledge from social networks can be used to enhance the communication processes. We focus on the use of identifying modular structure in social networks to improve the efficiency of routing strategies. Since nodes mobility in a network often alters its modular structure and requires recomputing of modules from scratch, updating the modules is the main bottleneck in current social-aware routing strategies where nodes often have limited processing speed. Towards real-time routing strategies, we develop an adaptive method to efficiently update modules in a dynamic network in which a novel compact representation of the network is used to significantly reduces the network size while preserving essential network structure.

Among existing countermeasures against Reactive Jamming Attacks in Wireless Sensor Networks, the detection of trigger nodes whose transmissions invoke the jammer nodes has been proposed and developed as an efficient jamming-resistent routing scheme in unreliable WSN environments. Sequential group testing techniques were adopted in our previous work to alleviate the overhead of trigger detection in terms of time and communication complexity. In this paper, we further improve the detection procedure by leveraging the classic models of randomized non-adaptive group testing and clique-independent set, which dramatically decreases the time complexity compared to our previous solution.

Virtual backbone has been proposed as the routing infrastructure to alleviate the broadcasting storm problem in ad hoc networks. Since the nodes in the virtual backbone need to carry other node's traffic, and node and link failure are inherent in wireless networks, it is desirable that the virtual backbone is fault tolerant. In this paper, we propose a new algorithm called Connecting Dominating Set Augmentation (CDSA) to construct a 2-connected virtual backbone which can resist the failure of one wireless node. We show that CDSA has guaranteed quality by proving that the size of the CDSA constructed 2-connected backbone is within a constant factor of the optimal 2-connected virtual backbone size. Through extensive simulations, we demonstrate that in practice, CDSA can build a 2-connected virtual backbone with only small overhead.

Despite decades of research, there have not been developed concrete defense solutions for most of current attacks to Internet services, let alone new attack types. An essential problem to overcome is that malicious traffic can be similar to legitimate ones. Thus a more fundamental model which should be based on the overall performance of servers/subnets without inspecting each traffic must be remedied. Based on this observation, we propose a novel system framework, called detection of malicious users (DMU) which attempts to solve various attack types. Motivated by DMU, we introduce a new theoretical model, called size constraint group testing (SCGT). Several algorithms based on SCGT for various networking scenarios are proposed. We also provide several fundamental results on SCGT, revealing some necessary conditions to obtain an O(1) detection time algorithm.

Broadcast scheduling is a fundamental problem in wireless ad hoc networks. The objective of a broadcast schedule is to deliver a message from a given source to all other nodes in a minimum amount of time. At the same time, in order for the broadcast to proceed as predicted in the schedule, it must not contain parallel transmissions which can be conflicting based on the collision and interference parameters in the wireless network. Most existing work on this problem use a limited network model which accounts only for conflicts occurring inside the transmission ranges of the nodes. The broadcast schedules produced by these algorithms are likely to experience unpredictable delays when deployed in the network. This is because they do not take into consideration other important sources of conflict in parallel transmissions, namely the interference range and the carrier sensing range. In this paper we develop a conflict-aware network model, which uses these parameters to increase the probability of scheduling conflict-free transmissions, and thereby improve the reliability of the broadcast schedule. We present and prove correctness of a constant approximation algorithm for minimum-latency broadcast scheduling under this network model. We also present a greedy heuristic algorithm for the same problem. Experimental results are provided to evaluate the performance of our algorithms. In addition, the algorithms are analyzed to justify their performance trends.

Since there is no fixed infrastructure in wireless ad hoc networks , virtual backbone has been proposed as the routing infrastructure to alleviate the broadcasting storm problem. The virtual backbone construction has been studied extensively in undirected graphs, especially in unit disk graphs, in which each node has the same transmission range. In practice, however, transmission ranges of all nodes are not necessarily equal. In this paper, we model such a network as a disk graph, where unidirectional links are considered. To study the virtual backbone construction in disk graphs, we consider two problems: strongly connected dominating set (SCDS) and strongly connected dominating and absorbing set (SCDAS). We propose a constant approximation algorithm and discuss its improvements for the SCDS problem . We also propose a heuristic for the SCDAS problem. Through extensive simulations, we verify our theoretical analysis and also demonstrate that the SCDS can be extended to form an SCDAS with marginal extra overhead.

This paper introduces the problem of fault tolerant topology control for all-to-one and one-to-all communication in static wireless networks with asymmetric wireless links. This problem is important in both theoretical and practical aspects. We investigate two approaches, namely minimum weight based approach and nearest neighbor augmentation approach, to address this problem. Furthermore, we give theoretical analysis for the proposed algorithms. Among other results, we show that the minimum weight based approach has a $k$-approximation algorithm for all-to-one fault tolerant topology control where $k$ is the number of disjoint paths. When $k=1$, this approach solves the minimum power all-to-one $1$-connected topology control problem. To the best of our knowledge, this paper is the first to study the fault tolerant topology control for all-to-one and one-to-all communication in asymmetric static wireless networks, and also is the first to demonstrate that the minimum power all-to-one 1-connected topology control problem has an optimal solution.

Network lifetime is a critical issue in Wireless Sensor Networks. It is possible to extend network lifetime by organizing the sensors into a number of sensor covers. However, with the limited bandwidth, coverage breach (i.e, targets that are not covered) can occur if the number of available time-slots/channels is less than the number of sensors in a sensor cover. In this paper, we study a joint optimization problem in which the objective is to minimize the coverage breach as well as to maximize the network lifetime. We show a "trade-off" scheme by presenting two strongly related models, which aim to tradeoffs between the two conflicting objectives. The main approach of our models is organizing sensors into non-disjoint sets, which is different from the current most popular approach and can gain longer network lifetime as well as less coverage breach. We proposed two algorithms for the first model based on linear programming and greedy techniques, respectively. Then we transform these algorithms to solve the second model by revealing the strong connection between the models. Through numerical simulation, we showed the good performance of our algorithms and the pictures of the tradeoff scheme in variant scenarios, which coincide with theoretical analysis very well. It is also showed that our algorithms could obtain less breach rate than the one proposed in [2].

In this paper, we study the Maximum lifetime Target Coverage problem (MTC), which is to maximize the network lifetime while guaranteeing the complete coverage of all the targets. Many centralized algorithms have been proposed to solve this problem. A very few distributed versions have also been presented but none of them obtains a good approximation ratio. In this paper, we propose two O(log n) localized algorithms. In particular, we first reduce the MTC problem to the domatic number problem in directed graphs. This relation shows that a feasible solution to the domatic number problem is also a feasible solution to the MTC problem. We next prove the lower and upper bounds of this domatic number. Based on this proof, we present two O(log n)-localized algorithms to solve the MTC problem.

Unlike a cellular or wired network, there is no base station or network infrastructure in a wireless ad-hoc network, in which nodes communicate with each other via peer communications. In order to make routing and flooding efficient in such an infrastructureless network, Connected Dominating Set (CDS) as a virtual backbone has been extensively studied. Most of the existing studies on the CDS problem have focused on unit disk graphs, where every node in a network has the same transmission range. However, nodes may have different powers due to difference in functionalities, power control, topology control, and so on. In this case, it is desirable to model such a network as a disk graph where each node has different transmission range. In this paper, we define Minimum Strongly Connected Dominating and Absorbent Set (MSCDAS) in a disk graph, which is the counterpart of minimum CDS in unit disk graph. We propose a constant approximation algorithm when the ratio of the maximum to the minimum in transmission range is bounded. We also present two heuristics and compare the performances of the proposed schemes through simulation.

Since there is no fixed infrastructure or centralized management in wireless ad hoc networks, a Connected Dominating Set (CDS) has been proposed to serve as a virtual backbone. The CDS of a graph representing a network has a significant impact on the efficient design of routing protocols in wireless networks. This problem has been studied extensively in Unit Disk Graphs (UDG), in which all nodes have the same transmission ranges. However, in practice, the transmission ranges of all nodes are not necessarily equal. In this paper, we model a network as a disk graph and introduce the CDS problem in disk graphs. We present two efficient approximation algorithms to obtain a minimum CDS. The performance ratio of these algorithms is constant if the ratio of the maximum transmission range over the minimum transmission range in the network is bounded. These algorithms can be implemented as distributed algorithms. Furthermore, we show a size relationship between a maximal independent set and a CDS as well as a bound of the maximum number of independent neighbors of a node in disk graphs. The theoretical analysis and simulation results are also presented to verify our approaches.

Summary form only given. Wireless ad hoc networks, including wireless sensor networks, can be flexibly and quickly deployed for many applications, both in military and civil. There is no fixed or predefined infrastructure in these networks. Virtual backbone has been proposed as routing infrastructure to alleviate the broadcasting storm problem as well as to reduce the energy consumption in communication. A virtual backbone can be approximated by a connected dominating set (CDS) of a graph representing a network. The problem of finding a minimum CDS is very challenging due to its hardness. More complicated, since the nodes in the virtual backbone need to carry other nodes traffic and node/link failure are inherent in wireless networks, it is desirable that the CDS is fault tolerant. In this talk, the author presents a novel algorithm to construct a 2-connected dominating set, in which for any pair of nodes, there exists at least 2 disjoint paths

Energy conservation is an important concern in wireless networks. Many algorithms for constructing a broadcast tree with minimum energy consumption and other goals have been developed. However, no previous research work considers the total energy consumption and transmission delays of the broadcast tree simultaneously. In this paper, based on an (alpha, beta)-tree, a novel concept to wireless networks, we define a new strongly connected broadcast arborescence with bounded transmission delay (SBAT) problem and design the strongly connected broadcast arborescence (SBA) algorithm with linear running time to construct a strongly connected broadcast tree with bounded total power, while satisfying the constraint that the transmission delays between the source and the other hosts are also bounded. We also propose the distributed version of the SBA algorithm. The theoretical analysis and simulation results show that the SBA algorithm gives a proper solution to the SBAT problem

A critical aspect of applications with wireless sensor networks is network lifetime. Power-constrained wireless sensor networks are usable as long as they can communicate sensed data to a processing node. Sensing and communications consume energy, therefore judicious power management and sensor scheduling can effectively extend network lifetime. To cover a set of targets with known locations when ground access in the remote area is prohibited, one solution is to deploy the sensors remotely, from an aircraft. The lack of precise sensor placement is compensated by a large sensor population deployed in the drop zone, that would improve the probability of target coverage. The data collected from the sensors is sent to a central node (e.g. cluster head) for processing. In this paper we propose un efficient method to extend the sensor network life time by organizing the sensors into a maximal number of set covers that are activated successively. Only the sensors from the current active set are responsible for monitoring all targets and for transmitting the collected data, while all other nodes are in a low-energy sleep mode. By allowing sensors to participate in multiple sets, our problem formulation increases the network lifetime compared with related work [M. Cardei et al], that has the additional requirements of sensor sets being disjoint and operating equal time intervals. In this paper we model the solution as the maximum set covers problem and design two heuristics that efficiently compute the sets, using linear programming and a greedy approach. Simulation results are presented to verify our approaches.

Due to the limited power supplies of a wireless node, energy efficiency is a crucial aspect to the design of a broadcast protocol. In the minimum energy broadcast problem, each node adjusts its transmission power to minimize the total energy consumption. The minimum energy broadcast problem is proved to be NP-Complete. The Hitch-hiking model introduced recently in [M. Agarwal et al., (2004)] takes advantage of the physical layer to combine partial signals containing the same data in order to decode a complete message. Moreover, the wireless multicast advantage (WMA), that is a single transmission can be received by all the nodes that are within the transmission range of a transmitting node, reduces the total energy of the broadcast tree. In this paper, we take advantages of both Hitch-hiking and WMA to design an energy-efficient broadcast tree algorithm with Hitch-hiking (BHH). The simulation results show that BHH reduces the total energy of the broadcast tree greatly.

Abstract Since no fixed infrastructure and no centralized management present in wireless networks, a connected dominating set (CDS) of the graph representing the network is widely used as a virtual backbone. Constructing a minimum CDS is NP‐hard. In this paper, we propose a new greedy algorithm, called S‐MIS, with the help of Steiner tree that can construct a CDS within a factor of 4.8 + ln5 from the optimal solution. We also introduce the distributed version of this algorithm. We prove that the proposed algorithm is better than the current best performance ratio which is 6.8. A simulation is conducted to compare S‐MIS with its variation which is rS‐MIS. The simulation shows that the sizes of the CDSs generated by S‐MIS and rS‐MIS are almost the same. Copyright © 2005 John Wiley & Sons, Ltd.