Graph property detection aims to determine whether a graph exhibits certain structural properties, such as being Hamiltonian. Recently, learning-based approaches have shown great promise by leveraging data-driven models to detect graph properties efficiently. In particular, vision-based methods offer a visually intuitive solution by processing the visualizations of graphs. However, existing vision-based methods rely on fixed visual graph layouts, and therefore, the expressiveness of their pipeline is restricted. To overcome this limitation, we propose VSAL, a vision-based framework that incorporates an adaptive layout generator capable of dynamically producing informative graph visualizations tailored to individual instances, thereby improving graph property detection. Extensive experiments demonstrate that VSAL outperforms state-of-the-art vision-based methods on various tasks such as Hamiltonian cycle, planarity, claw-freeness, and tree detection.

Large Language Model-based multi-agent systems demonstrate strong capabilities across different tasks. However, current methods often rely on static or task-specific designs. These instance-agnostic methods overlook varying instance complexities, leading to redundant communication in simple scenarios and insufficient coordination in demanding ones. Consequently, they failed to achieve effective and efficient collaborative reasoning among agents. To overcome these limitations, we propose Difficulty-Aware Graph Pruning (DAGP), an adaptive framework that configures communication structures based on instance-specific difficulty. DAGP integrates a difficulty estimation module and a sparsity control mechanism to selectively activate communication edges based on each instance, promoting efficient and targeted collaboration. Empirical evaluations across diverse benchmarks demonstrate that DAGP consistently achieves state-of-the-art performance compared to other baselines while reducing average token usage by 45%.

Hypergraph neural networks have been promising tools for handling learning tasks involving higher-order data, with notable applications in web graphs, such as modeling multi-way hyperlink structures and complex user interactions. Yet, their generalization abilities in theory are less clear to us. In this paper, we seek to develop margin-based generalization bounds for four representative classes of hypergraph neural networks, including convolutional-based methods (UniGCN), set-based aggregation (AllDeepSets), invariant and equivariant transformations (M-IGN), and tensor-based approaches (T-MPHN). Through the PAC-Bayes framework, our results reveal the manner in which hypergraph structure and spectral norms of the learned weights can affect the generalization bounds, where the key technical challenge lies in developing new perturbation analysis for hypergraph neural networks, which offers a rigorous understanding of how variations in the model's weights and hypergraph structure impact its generalization behavior. Our empirical study examines the relationship between the practical performance and theoretical bounds of the models over synthetic and real-world datasets. One of our primary observations is the strong correlation between the theoretical bounds and empirical loss, with statistically significant consistency in most cases.

Data-driven approaches have been proven effective in solving combinatorial optimization problems over graphs such as the traveling salesman problems and the vehicle routing problem. The rationale behind such methods is that the input instances may follow distributions with salient patterns that can be leveraged to overcome the worst-case computational hardness. For optimization problems over graphs, the common practice of neural combinatorial solvers consumes the inputs in the form of adjacency matrices. In this paper, we explore a vision-based method that is conceptually novel: can neural models solve graph optimization problems bytaking a look at the graph pattern - Our results suggest that the performance of such vision-based methods is not only non-trivial but also comparable to the state-of-the-art matrix-based methods, which opens a new avenue for developing data-driven optimization solvers.

Vehicle tracking, a core application to smart city video analytics, is becoming more widely deployed than ever before thanks to the increasing number of traffic cameras and recent advances in computer vision and machine-learning. Due to the constraints of bandwidth, latency, and privacy concerns, tracking tasks are more preferable to run on edge devices sitting close to the cameras. However, edge devices are provisioned with a fixed amount of computing budget, making them incompetent to adapt to time-varying and imbalanced tracking workloads caused by traffic dynamics. In coping with this challenge, we propose WatchDog, a real-time vehicle tracking system that fully utilizes edge nodes across the road network. WatchDog leverages computer vision tasks with different resource-accuracy tradeoffs, and decomposes and schedules tracking tasks judiciously across edge devices based on the current workload to maximize the number of tasks while ensuring a provable response time-bound at each edge device. Extensive evaluations have been conducted using real-world city-wide vehicle trajectory datasets, achieving exceptional tracking performance with a real-time guarantee.

Modeling the spread of social contagions is central to various applications in social computing. In this paper, we study the learnability of the competitive threshold model from a theoretical perspective. We demonstrate how competitive threshold models can be seamlessly simulated by artificial neural networks with finite VC dimensions, which enables analytical sample complexity and generalization bounds. Based on the proposed hypothesis space, we design efficient algorithms under the empirical risk minimization scheme. The theoretical insights are finally translated into practical and explainable modeling methods, the effectiveness of which is verified through a sanity check over a few synthetic and real datasets. The experimental results promisingly show that our method enjoys a decent performance without using excessive data points, outperforming off-the-shelf methods.

Considering two decision-making tasks $A$ and $B$, each of which wishes to compute an effective decision $Y$ for a given query $X$, can we solve task $B$ by using query-decision pairs $(X, Y)$ of $A$ without knowing the latent decision-making model? Such problems, called inverse decision-making with task migrations, are of interest in that the complex and stochastic nature of real-world applications often prevents the agent from completely knowing the underlying system. In this paper, we introduce such a new problem with formal formulations and present a generic framework for addressing decision-making tasks in social contagion management. On the theory side, we present a generalization analysis for justifying the learning performance of our framework. In empirical studies, we perform a sanity check and compare the presented method with other possible learning-based and graph-based methods. We have acquired promising experimental results, confirming for the first time that it is possible to solve one decision-making task by using the solutions associated with another one.

Online discussion forums are trending as popular platforms that allow asynchronous online interactions through a unique communication structure composed of main threads and the associated replies. In this paper, we present a learning framework – called SocialGrid – for modeling event dynamics in online discussion forums. Using a grid transformation, we explore the possibility of converting the problem of tempo-ral space modeling into the problem of density space modeling. Inspired by the nature of the grid, we leverage a temporal convolution network to learn the dynamics in the density space. Changing the transformation precision, our approach can model the temporal dynamics at different granularities, thereby fulfilling prediction tasks with different needs. Experiments on real-world datasets have shown that our frame-work excels at various prediction tasks compared with other possible approaches.

Real-world decision-making systems are often subject to uncertainties that have to be resolved through observational data. Therefore, we are frequently confronted with combinatorial optimization problems of which the objective function is unknown and thus has to be debunked using empirical evidence. In contrast to the common practice that relies on a learning-and-optimization strategy, we consider the regression between combinatorial spaces, aiming to infer high-quality optimization solutions from samples of input-solution pairs -- without the need to learn the objective function. Our main deliverable is a universal solver that is able to handle abstract undetermined stochastic combinatorial optimization problems. For learning foundations, we present learning-error analysis under the PAC-Bayesian framework using a new margin-based analysis. In empirical studies, we demonstrate our design using proof-of-concept experiments, and compare it with other methods that are potentially applicable. Overall, we obtain highly encouraging experimental results for several classic combinatorial problems on both synthetic and real-world datasets.

The well-known influence maximization problem (IM) aims at maximizing the influence of one information cascade in a social network by selecting appropriate seed users prior to the diffusion process. In its adaptive version, additional seed users can be selected after observing certain diffusion results. On the other hand, social computing tasks are often time-critical, and therefore, only the influence resulted in the early period is worthwhile, which can be naturally modeled by enforcing a time constraint. In this article, we present an analysis of the time-constrained adaptive IM problem. On the theory side, we provide the hardness results of computing the optimal policy and a lower bound on the adaptive gap, which measures the superiority of adaptive policies over the nonadaptive policies. For practical solutions, from basic to advanced, we design a series of seeding policies for achieving high efficacy and scalability. Finally, we investigate the proposed solutions through extensive simulations based on real-world data sets.

An echo chamber effect describes the situation in which opinions are amplified by communication and repetition inside a relatively closed social system. In this article, we will detect the echo chamber effect in real-world data set and measure this effect during the information diffusion process. Any user will be influenced by its neighbors or echo chamber effect. Also, we assume that these activation events from each activated neighbor and from echo chamber effect are independent. In this article, we detect and model the echo chamber effect for the first time. Then, the influence maximization with echo chamber (IMEC) problem aims to select$k$users to propagate information such that the expected number of activated users is maximized. We formulate this problem using a graph model and analyze the NP-hardness. Second, the objective of IMEC as a set function is proved to be neither submodular nor supermodular. Then, an improved greedy algorithm is proposed, which is combined metaheuristic strategies. Finally, experimental results show that our algorithm is effective in detecting echo chamber effect and efficiency in selecting seed nodes.

Given a combinatorial optimization problem taking an input, can we learn a strategy to solve it from the examples of input-solution pairs without knowing its objective function? In this paper, we consider such a setting and study the misinformation prevention problem. Given the examples of attacker-protector pairs, our goal is to learn a strategy to compute protectors against future attackers, without the need of knowing the underlying diffusion model. To this end, we design a structured prediction framework, where the main idea is to parameterize the scoring function using random features constructed through distance functions on randomly sampled subgraphs, which leads to a kernelized scoring function with weights learnable via the large margin method. Evidenced by experiments, our method can produce near-optimal protectors without using any information of the diffusion model, and it outperforms other possible graph-based and learning-based methods by an evident margin.

Device-free indoor localization may play a critical role in improving passengers' safety in large vessels, particularly for scenarios without equipped radios. However, due to dynamic internal and external influences from the sailing ship such as changing sailing speed, the existing localization systems suffer huge accuracy degradation in a mobile ship environment. The challenges are mainly due to rich and arbitrary ship motions and the resulting complicated impacts on the indoor wireless channels. To address the challenges, in this article, we first propose a ship motion descriptor to extract discriminative latent representation from complex ship motions by leveraging deep-learning techniques. Based on this representation, we then design a novel fingerprint roaming model, i.e., MoLoc, to automatically learn the predictive fingerprint variation pattern and transfer the online fingerprint measurement to adapt to dynamic ship motions in real time. Furthermore, an unsupervised learning strategy is proposed to train the fingerprint roaming model using unlabeled onboard collected data which do not incur any labor costs. We have implemented and extensively evaluated MoLoc on real-world cruise ships, where experimental results demonstrate that MoLoc improves localization accuracy from 63.2% to 92.8% compared to the state-of-the-art localization methods, including Pilot, LiFS, SpotFi, and AutoFi while achieving a mean error of 0.68 m.

In Online Social Networks (OSNs), privacy issue is a growing concern as more and more users are sharing their candid personal information and friendships online. One simple yet effective attack aims at private user data is to use socialbots to befriend the users and crawl data from users who accept the attackers' friend requests. With the attackers involving, individual users' preference and habit analysis is available, hence it is easier for the attackers to trick the users and befriend them. To better protect private information, some cautious, high-profile users may refer to their friends' decisions when receiving a friend request. The aim for this paper is to analyze the vulnerability of OSN users under this attack, in a more realistic setting that the high profile users having a different friend request acceptance model. Specifically, despite the existing probabilistic acceptance models, we introduce a deterministic linear threshold acceptance model for the cautious users such that they will only accept friend requests from users sharing at least a certain number of mutual friends with them. The model makes the cautious users harder to befriend with and complicates the attack. Although the new problem with multiple acceptance models is non-submodular and has no performance guarantee in general, we introduce the concept of adaptive submodular ratio and establish an approximation ratio under certain conditions. In addition, our results are also verified by extensive experiments in real-world OSN data sets.

Guiding users to actively expanding their online social circles is one of the primary strategies for enhancing user participation and growing online social networks. In this paper, we study the active friending problem which aims at providing users with the strategy for methodically sending invitations to successfully build a friendship with target users. We consider the prominent linear threshold model for the friending process and formulate the active friending problem as an optimization problem. The key observation is the relationship between the active friending problem and the minimum subset cover problem, based on which we present the first randomized algorithm with a data-independent approximation ratio and a controllable success probability for general graphs. The performance of the proposed algorithm is theoretically analyzed and supported by encouraging simulation results done on extensive datasets.

Online misinformation has been considered as one of the top global risks as it may cause serious consequences such as economic damages and public panic. The misinformation prevention problem aims at generating a positive cascade with appropriate seed nodes in order to compete against the misinformation. In this paper, we study the misinformation prevention problem under the prominent independent cascade model. Due to the #P-hardness in computing influence, the core problem is to design effective sampling methods to estimate the function value. The main contribution of this paper is a novel sampling method. Different from the classic reverse sampling technique which treats all nodes equally and samples the node uniformly, the proposed method proceeds with a hybrid sampling process which is able to attach high weights to the users who are prone to be affected by the misinformation. Consequently, the new sampling method is more powerful in generating effective samples used for computing seed nodes for the positive cascade. Based on the new hybrid sample technique, we design an algorithm offering a (1-1/e-€)-approximation. We experimentally evaluate the proposed method on extensive datasets and show that it significantly outperforms the state-of-the-art solutions.

The widespread online misinformation could cause public panic and serious economic damages. The misinformation containment problem aims at limiting the spread of misinformation in online social networks by launching competing campaigns. Motivated by realistic scenarios, we present the first analysis of the misinformation containment problem for the case when an arbitrary number of cascades are allowed. This paper makes four contributions. First, we provide a formal model for multi-cascade diffusion and introduce an important concept called as cascade priority. Second, we show that the misinformation containment problem cannot be approximated within a factor of $\Omega(2^{\log^{1-\epsilon}n^4})$ in polynomial time unless $NP \subseteq DTIME(n^{\polylog{n}})$. Third, we introduce several types of cascade priority that are frequently seen in real social networks. Finally, we design novel algorithms for solving the misinformation containment problem. The effectiveness of the proposed algorithm is supported by encouraging experimental results.

Misinformation and rumor can spread rapidly and widely through online social networks and therefore rumor controlling has become a critical issue. It is assumed in the existing works that there is a single authority whose goal is to minimize the spread of rumor by generating a positive cascade. In this paper, we study a more realistic scenario when there is multiple positive cascades generated by different agents. For the multiple-cascade diffusion, we propose the peer-to-peer independent cascade model for private social communications. The main contribution of this paper is an analysis of the rumor blocking effect (i.e., the number of the users activated by rumor) when the agents noncooperatively generate the positive cascades. We show that the rumor blocking effect provided by the Nash equilibrium will not be arbitrarily worse even if the positive cascades are generated noncooperatively. In addition, we give a discussion on how the cascade priority and activation order affect the rumor blocking problem. We experimentally examine the Nash equilibrium of the proposed games by simulations done on real social network structures.

Social networks allow rapid spread of ideas and innovations while the negative information can also propagate widely. When the cascades with different opinions reaching the same user, the cascade arriving first is the most likely to be taken by the user. Therefore, once misinformation or rumor is detected, a natural containment method is to introduce a positive cascade competing against the rumor. Given a budget k, the rumor blocking problem asks for k seed users to trigger the spread of the positive cascade such that the number of the users who are not influenced by rumor can be maximized. The prior works have shown that the rumor blocking problem can be approximated within a factor of (1 - 1/e- δ) by a classic greedy algorithm combined with Monte Carlo simulation with the running time of O(k3mn ln n/δ2), where n and m are the number of users and edges, respectively. Unfortunately, the Monte-Carlo-simulation-based methods are extremely time consuming and the existing algorithms either trade performance guarantees for practical efficiency or vice versa. In this paper, we present a randomized algorithm which runs in O(km ln n/δ2) expected time and provides a (1 - 1/e - δ)-approximation with a high probability. The experimentally results on both the real-world and synthetic social networks have shown that the proposed randomized rumor blocking algorithm is much more efficient than the state-of-the-art method and it is able to find the seed nodes which are effective in limiting the spread of rumor.

One model for viral marketing is the positive influence. In this model, an inactive node is changed into active if and only if at least half of its neighbors are already in active state. The positive influence model can be viewed as a special case of a general threshold model, in which the threshold function at each node has value one if at least a certain fraction of neighbors are in active state, and value 0 otherwise. This function can be proved to be monotonically increasing and nonsubmodular for any predefined fraction. Therefore, given a seed set, the number of influenced nodes is not submodular with respect to the size of the seed set. This fact makes those optimization problems related with positive influence very hard, including the minimum partial positive influence seeding problem: Given a social network G = (V, E) and a number 02H ([pn]))-approximation algorithm for the minimum partial positive influence seeding problem, where n is the number of nodes, and H(·) is the Harmonic number.

For the purpose of propagating information and ideas through a social network, a seeding strategy aims to find a small set of seed users that are able to maximize the spread of the influence, which is termed influence maximization problem. Despite a large number of works have studied this problem, the existing seeding strategies are limited to the models that cannot fully capture the characteristics of real-world social networks. In fact, due to high-speed data transmission and large population of participants, the diffusion processes in real-world social networks have many aspects of uncertainness. As shown in the experiments, when taking such uncertainness into account, the state-of-the-art seeding strategies are pessimistic as they fail to trace the influence diffusion. In this paper, we study the strategies that select seed users in an adaptive manner. We first formally model the dynamic independent Cascade model and introduce the concept of adaptive seeding strategy. Then, based on the proposed model, we show that a simple greedy adaptive seeding strategy finds an effective solution with a provable performance guarantee. Besides the greedy algorithm, an efficient heuristic algorithm is provided for better scalability. Extensive experiments have been performed on both the real-world networks and synthetic power-law networks. The results herein demonstrate the superiority of the adaptive seeding strategies to other baseline methods.

Community detection aims to reveal the community structure in a social network, which is one of the fundamental problems. In this paper we investigate the community detection problem based on the concept of terminal set. A terminal set is a group of users within which any two users belong to different communities. Although the community detection is hard in general, the terminal set can be very helpful in designing effective community detection algorithms. We first present a 2-approximation algorithm running in polynomial time for the original community detection problem. In the other issue, in order to better support real applications we further consider the case when extra restrictions are imposed on feasible partitions. For such customized community detection problems, we provide two randomized algorithms which are able to find the optimal partition with a high probability. Demonstrated by the experiments performed on benchmark networks the proposed algorithms are able to produce high-quality communities.

In a social network, influence diffusion is the process of spreading innovations from user to user. An activation state identifies who are the active users who have adopted the target innovation. Given an activation state of a certain diffusion, effector detection aims to reveal the active users who are able to best explain the observed state. In this paper, we tackle the effector detection problem from two perspectives. The first approach is based on the influence distance that measures the chance that an active user can activate its neighbors. For a certain pair of users, the shorter the influence distance, the higher probability that one can activate the other. Given an activation state, the effectors are expected to have short influence distance to active users while long to inactive users. By this idea, we propose the influence-distance-based effector detection problem and provide a 3-approximation. Second, we address the effector detection problem by the maximum likelihood estimation (MLE) approach. We prove that the optimal MLE can be obtained in polynomial time for connected directed acyclic graphs. For general graphs, we first extract a directed acyclic subgraph that can well preserve the information in the original graph and then apply the MLE approach to the extracted subgraph to obtain the effectors. The effectiveness of our algorithms is experimentally verified via simulations on the real-world social network.

Uniform heterogeneous multicore architectures are becoming increasingly popular due to their potential of achieving high performance and energy efficiency compared to the homogeneous multicore architectures. In such systems, the real-time scheduling problem becomes more challenging because processors have different speeds. Prior research on uniform heterogeneous multiprocessor real-time scheduling has focused on hard real-time systems, where, significant processing capacity may have to be sacrificed in the worst-case to ensure that all deadlines are met. As meeting hard deadlines is overkill for many soft real-time systems in practice, this paper shows that on soft real-time uniform heterogeneous multiprocessors, bounded response times can be ensured for globally-scheduled sporadic task systems with no utilization loss. A GEDF-based scheduling algorithm, named as GEDF-H, is presented and response time bounds are established under both preemptive and non-preemptive GEDF-H scheduling. Extensive experiments show that the magnitude of the derived response time bound is reasonable, often smaller than four task relative deadlines. To the best of our knowledge, this paper is the first to show that soft real-time sporadic task systems can be supported on uniform heterogeneous multiprocessors without utilization loss under global scheduling, and with reasonable predicted response times.

Graphics processing units (GPUs) are being widely used as co-processors in many application domains to accelerate general-purpose workloads that are computationally intensive, known as GPGPU computing. Real-time multi-tasking support is a critical requirement for many emerging GPGPU computing domains. However, due to the asynchronous and non-preemptive nature of GPU processing, in multi-tasking environments, tasks with higher priority may be blocked by lower priority tasks for a lengthy duration. This severely harms the system's timing predictability and is a serious impediment limiting the applicability of GPGPU in many real-time and embedded systems. In this paper, we present an efficient GPGPU preemptive execution system (GPES), which combines user-level and driverlevel runtime engines to reduce the pending time of high-priority GPGPU tasks that may be blocked by long-freezing low-priority competing workloads. GPES automatically slices a long-running kernel execution into multiple subkernel launches and splits data transaction into multiple chunks at user-level, then inserts preemption points between subkernel launches and memorycopy operations at driver-level. We implement a prototype of GPES, and use real-world benchmarks and case studies for evaluation. Experimental results demonstrate that GPES is able to reduce the pending time of high-priority tasks in a multitasking environment by up to 90% over the existing GPU driver solutions, while introducing small overheads.

In many embedded real-time systems, applications often interact with I/O devices via read/write operations, which may incur considerable suspension delays. Unfortunately, prior analysis methods for validating timing correctness in embedded systems become quite pessimistic when suspension delays are present. In this paper, we consider the problem of supporting two common types of I/O applications in a multiprocessor system, that is, write-only applications and read-write applications. For the write-only application model, we present a much improved analysis technique that results in only O(m) suspension-related utilization loss, where m is the number of processors. For the second application model, we present a flexible I/O placement strategy and a corresponding new scheduling algorithm, which can completely circumvent the negative impact due to read- and write-induced suspension delays. We illustrate the feasibility of the proposed I/O-placement-based schedule via a case study implementation. Furthermore, experiments presented herein show that the improvement with respect to system utilization over prior methods is often significant.