Finding frequent subgraph patterns in a big graph is an important problem with many applications such as classifying chemical compounds and building indexes to speed up graph queries. Since this problem is NP-hard, some recent parallel and distributed systems have been developed to accelerate the mining. However, they often have a huge memory cost, very long running time, suboptimal load balancing, poor scale-out capability, and possibly inaccurate results. In this article, we propose an efficient system called T-FSM for parallel mining of frequent subgraph patterns in a big graph. T-FSM supports a new anti-monotonic frequentness measure called Fraction-Score, which is more accurate than the widely used MNI measure. The execution engine of T-FSM supports both intra-machine parallelism and inter-machine parallelism. For intra-machine parallelism, T-FSM adopts a novel task-based execution model to ensure high multithreading concurrency, bounded memory consumption, and effective load balancing. For inter-machine parallelism, T-FSM ensures good scale-out performance with a lightweight pattern rebalancing approach that reduces workload skewness of pattern evaluations among machines. To avoid recomputing the contexts for migrated patterns, we design a novel context cache table to support concurrent and asynchronous requesting and caching of remote context data, which can timely evict and garbage collect used pattern contexts that are no longer needed to keep memory consumption bounded. Extensive experiments show that T-FSM is orders of magnitude faster than existing state-of-the-art parallel systems (more than 10×, 51×, 131×, 55× speedup over ScaleMine, DistGraph, Pangolin and Peregrine, respectively) and distributed systems (more than 42× and 88× over ScaleMine and DistGraph, respectively) for frequent subgraph pattern mining, and it scales out satisfactorily to 512 CPU cores on the Polaris supercomputer at Argonne National Laboratory.

Accurate and prompt mapping of flood extent is important for effective disaster management. Prior terrain-guided methods utilizing digital elevation model (DEM) data to improve segmentation quality, but are only applicable to small areas with restricted assumptions. We propose ZoneGraph, a scalable flood mapping approach that is generally applicable to any area. Since water level varies a lot from upstream to downstream, and an Earth image may contain multiple river channels or branches, ZoneGraph partitions an Earth image into localized zones each with a consistent water level for intra-region inference using hidden Markov trees (HMTs), and uses a zone adjacency graph for inter-zone regularization to enforce flow direction consistency across adjacent zones. Parallelization techniques are applied to speed up the zonal water level computations. Experiments show ZoneGraph achieves higher accuracy than existing methods, scale to a large area with 24,805 × 40,129 pixels, and yields up to 14.3× parallelization speedup on 32 threads.

The growing reliance on deep learning models in safety-critical domains such as healthcare and autonomous navigation underscores the need for defenses that are both robust to adversarial perturbations and transparent in their decision-making. In this paper, we identify a connection between interpretability and robustness that can be directly leveraged during training. Specifically, we observe that spurious, unstable, or semantically irrelevant features identified through Local Interpretable Model-Agnostic Explanations (LIME) contribute disproportionately to adversarial vulnerability. Building on this insight, we introduce an attribution-guided refinement framework that transforms LIME from a passive diagnostic into an active training signal. Our method systematically suppresses spurious features using feature masking, sensitivity-aware regularization, and adversarial augmentation in a closed-loop refinement pipeline. This approach does not require additional datasets or model architectures and integrates seamlessly into standard adversarial training. Theoretically, we derive an attribution-aware lower bound on adversarial distortion that formalizes the link between explanation alignment and robustness. Empirical evaluations on CIFAR-10, CIFAR-10-C, and CIFAR-100 demonstrate substantial improvements in adversarial robustness and out-of-distribution generalization.

Federated Learning (FL) is a promising distributed machine learning approach that enables collaborative training of a global model using multiple edge devices. The data distributed among the edge devices are highly heterogeneous. Thus, FL faces the challenge of data distribution and heterogeneity, where non-Independent and Identically Distributed (non-IID) data across edge devices may yield in significant accuracy drop. Furthermore, the limited computation and communication capabilities of edge devices increase the likelihood of stragglers, thus leading to slow model convergence. In this article, we propose the FedDHAD FL framework, which comes with two novel methods: dynamic heterogeneous model aggregation (FedDH) and adaptive dropout (FedAD). FedDH dynamically adjusts the weights of each local model within the model aggregation process based on the non-IID degree of heterogeneous data to deal with the statistical data heterogeneity. FedAD performs neuron-adaptive operations in response to heterogeneous devices to improve accuracy while achieving superb efficiency. The combination of these two methods makes FedDHAD significantly outperform state-of-the-art solutions in terms of accuracy (up to 6.7% higher), efficiency (up to 2.02 times faster), and computation cost (up to 15.0% smaller).

Mining dense subgraphs from a big graph is important in applications such as community (or module) detection in social (or biological) networks. While most dense structures are defined on undirected graphs, recent efforts have generalized these notions to directed graphs. In this demonstration paper, we present DirDense, an interactive tool that makes it easy for end-users to mine dense structures from a big directed graph. DirDense currently supports the mining of maximal (γ1, γ2)-quasi-cliques, maximal (k 1,k 1)-plexes, and the directed densest subgraph. DirDense facilitates parameter tuning for each type of the structure-mining tasks, and provides intuitive interfaces to visualize and examine the dense directed structures. Using real-world data, we showcase how users can mine dense directed structures by parameter tuning in DirDense, and how they can conveniently examine these structures and cascade the mining tasks to find progressively larger dense subgraphs more quickly.

Explaining black-box models is fundamental to gaining trust and deploying these models in real applications. As existing explanation methods have been shown to lack robustness against adversarial perturbations, there has been a growing interest in generating robust explanations. However, existing works resort to empirical defense strategies and these heuristic methods fail against powerful adversaries. In this paper, we certify the robustness of explanations motivated by the success of randomized smoothing. Specifically, we compute a tight radius in which the robustness of the explanation is certified. While a challenge is how to formulate the robustness of the explanation mathematically, we quantize the explanation into discrete spaces to mimic classification in randomized smoothing. To address the high computational cost of randomized smoothing, we introduce randomized gradient smoothing. Also, we explore the robustness of the semantic explanation by certifying the robustness of capsules. In the experiment, we demonstrate the effectiveness of our method on benchmark datasets from the perspectives of post-hoc explanation and semantic explanation respectively. Our work is a promising step towards filling the gap between the theoretical robustness bound and empirical explanations. Our code has been released at https://github.com/NKUShaw/CertifiedExplanation.

In this demonstration paper, we describe FSM-Explorer, an interactive tool that makes it easier for end-users to mine frequent subgraph patterns from a big graph$G$, and to explore the subgraph instances in$G$that match the patterns. FSM-Explorer not only supports the popular MNI support measure, but also the recently proposed Fraction-Score measure that is more accurate. Its backend engine is built on top of our recently developed T-FSM system that ensures high concurrency, bounded memory consumption, and effective load balancing. Using real-world data, we showcase how users can mine frequent subgraph patterns by parameter tuning in FSM-Explorer, and how they can conveniently examine the many matched instances in$G$one batch at a time to improve productivity.

Subgraph search problems such as maximal clique enumeration and subgraph matching generate a search-space tree which is traversed in depth-first manner by serial backtracking algorithms that are recursive. Since Jenkins et al. reported the backtracking paradigm to be sub-optimal for GPU acceleration, breadth-first traversal of the search-space tree is widely adopted by GPU algorithms. However, they produce a lot of intermediate subgraphs that exhaust the GPU device memory. Recent works revive the depth-first backtracking paradigm for GPU acceleration, where each warp is a basic processing unit with its own stack in device memory for subgraph backtracking. However, they adopt complicated methods for load balancing that incur a lot of overheads. They also use hardcoded fixed space for stacks that is determined ad-hoc and may lead to inaccuracy when the allocated space is insufficient. In this paper, we use subgraph matching as a case study to propose novel depth-first GPU solutions to address the above problems. Our approach, called T-DFS, decomposes computation into independent tasks that process search-space subtrees, which are managed by an efficient lock-free circular task queue. Tasks are distributed to different warps for parallel processing, and a novel timeout mechanism is used to eliminate straggler tasks to ensure load balancing. We also support flexible and fine-grained dynamic memory allocation for stack spaces to avoid the stack space allocation pitfalls of existing works. Extensive experiments on real graphs show that T-DFS significantly outperforms existing depth-first GPU solutions for the subgraph matching application.

Finding all those subgraphs of a big graph that satisfy certain conditions (aka. subgraph finding) is useful in many applications such as community detection and subgraph matching. These problems often generate a search-space tree with size exponential to the size of the input graph. GPUs with thousands of cores are a natural choice to speed up subgraph finding, but existing GPU solutions either conduct BFS on the search-space tree which leads to memory overflow due to intermediate subgraph-size explosion, or they conduct DFS on the search-space tree which is memory-efficient but can be 2 orders of magnitude slower than a BFS solution. In this paper, we present$\mathbf{G}^{2}$-AIMD, a subgraph-centric framework for efficient subgraph Search on GPUs, which enjoys the efficiency of BFS on the search-space tree, while avoids intermediate subgraph-size explosion with novel system designs such as adaptive chunk-size adjustment and host-memory subgraph buffering, inspired by the additive-increase/multiplicative-decrease (AIMD) algorithm in TCP congestion control.$\mathrm{G}^{2}$-AIMD provides a convenient subgraph-centric programming interface to facilitate the implementation of subgraph finding algorithms on top, so as to enjoy the above performance merits.$\mathbf{G}^{2}{-}$AIMD also supports multi-GPU execution where each GPU only needs to load a fraction of the input graph. To demonstrate the efficiency and scalability of$\mathbf{G}^{2}$-AIMD, two algorithms were implemented on top with additional optimization techniques, and they significantly outperform the existing GPU solutions.

Federated Learning (FL) has achieved significant achievements recently, enabling collaborative model training on distributed data over edge devices. Iterative gradient or model exchanges between devices and the centralized server in the standard FL paradigm suffer from severe efficiency bottlenecks on the server. While enabling collaborative training without a central server, existing decentralized FL approaches either focus on the synchronous mechanism that deteriorates FL convergence or ignore device staleness with an asynchronous mechanism, resulting in inferior FL accuracy. In this paper, we propose an Asynchronous Efficient Decentralized FL framework, i.e., AEDFL, in heterogeneous environments with three unique contributions. First, we propose an asynchronous FL system model with an efficient model aggregation method for improving the FL convergence. Second, we propose a dynamic staleness-aware model update approach to achieve superior accuracy. Third, we propose an adaptive sparse training method to reduce communication and computation costs without significant accuracy degradation. Extensive experimentation on four public datasets and four models demonstrates the strength of AEDFL in terms of accuracy (up to 16.3% higher), efficiency (up to 92.9% faster), and computation costs (up to 42.3% lower).

Despite achieving remarkable performance, Federated Learning (FL) encounters two important problems, i.e., low training efficiency and limited computational resources. In this article, we propose a new FL framework, i.e., FedDUMAP, with three original contributions, to leverage the shared insensitive data on the server in addition to the distributed data in edge devices so as to efficiently train a global model. First, we propose a simple dynamic server update algorithm, which takes advantage of the shared insensitive data on the server while dynamically adjusting the update steps on the server in order to speed up the convergence and improve the accuracy. Second, we propose an adaptive optimization method with the dynamic server update algorithm to exploit the global momentum on the server and each local device for superior accuracy. Third, we develop a layer-adaptive model pruning method to carry out specific pruning operations, which is adapted to the diverse features of each layer so as to attain an excellent tradeoff between effectiveness and efficiency. Our proposed FL model, FedDUMAP, combines the three original techniques and has a significantly better performance compared with baseline approaches in terms of efficiency (up to 16.9 times faster), accuracy (up to 20.4% higher), and computational cost (up to 62.6% smaller).

Online shopping has become a crucial way to encourage daily consumption, where the User-generated, or crowdsourced product comments, can offer a broad range of feedback on e-commerce products. As a result, integrating critical opinions or major attitudes from the crowdsourced comments can provide valuable feedback for marketing strategy adjustment or product-quality monitoring. Unfortunately, the scarcity of annotated ground truth on the integrated comment, or the limited gold integration reference, has incurred the infeasibility of the regular supervised-learning-based comment integration. To resolve this problem, in this article, inspired by the principle of Transfer Learning, we propose a three-stage transferable and generative crowdsourced comment integration framework ( TTGCIF ) based on zero-and-few-shot learning with the support of domain distribution alignment. The proposed framework aims at generating abstractive integrated comment in target domain via the enhanced neural text generation model, by referring the available integration resource in related source domains, to avoid the exhausted effort on resource annotation devoted to the target domain. Specifically, at the first stage, to enhance the domain transferability, representations on the crowdsourced comments have been aligned up between the source and target domain, by minimizing the domain distribution discrepancy in the kernel space. At the second stage, Zero-shot comment integration mechanism has been adopted to deal with the dilemma that none of the gold integration reference may be available in target domain. In other words, taking the sample-level semantic prototype as input, the enhanced neural text generation model in TTGCIF is trained to learn data semantic association among different domains via semantic prototype transduction, so that the “ unlabeled ” crowdsourced comments in target domain can be associated with existing integration references in related source domains. At the third stage, based on the parameters trained at the second stage, fast domain adaptation mechanism in a Few-shot manner has also been adopted by seeking most potential parameters along the gradient direction constrained by instances across multiple source domains. In this way, parameters in TTGCIF can be sensitive to any alteration on training data, ensuring that even if only few annotated resource in target domain are available for “Fine-tune,” TTGCIF can still react promptly to achieve effective target domain adaptation. According to the experimental results, TTGCIF can achieve the best transferable product comment integration performance in target domain, with fast and stable domain adaption effect depending on no more than 10% annotated resource in target domain. More importantly, even if TTGCIF has not been fine-tuned on the target domain, yet by referring to the available integration resource in related source domains, the integrated comments generated by TTGCIF on the target domain are still superior to those generated by models already fine-tuned on the target domain.

Crowdsourcing is a sourcing model where individuals or organizations obtain goods and services from a large, relatively open and often rapidly evolving group of internet users. The most common way that crowdsourcing can facilitate machine learning is to annotate instances with labels [1] . However, the same instance may have inconsistent class labels, in the eyes of various annotators. Therefore, current efforts in crowdsourcing mainly focus on the truth inference or label integration, to remove inconsistent labels or to alleviate biased labeling. In turn, instances with the integrated labels could facilitate the training on machine learning models. The future direction of crowdsourcing is to apply more fine-grained truth inference methods to different application domains [2] . Consequently, we evolve toward another challenging problem of comment integration. That is, how can we integrate or summarize the core opinions of multiple product comments obtained from users, rather than the discrete labels.

The past decade has witnessed wide applications of machine learning to various domains for decision-making, including crime detection, urban planning, drug discovery, and health monitoring, which benefited from surging data resources. As data collection in real-world applications is often done in different locations, being able to mine and discover knowledge from distributed data sources is an essential requirement for building powerful predictive models. However, directly uploading all data sources to an untrustworthy centralized data server for learning will lead to risks of privacy leakage. Federated Learning (FL) emerges as a decentralized learning framework that aggregates knowledge from distributed data without centralizing them, hence mitigating privacy risks. By hosting this workshop, we aim to attract a broad spectrum of audiences, including researchers and practitioners from academia and industry interested in the latest advances in FL. As an effort to advance the fundamental development of FL in data mining, this workshop will encourage ideas exchange on the trustworthiness, scalability, robustness, and broad applications of FL.

Quasi-cliques are a type of dense subgraphs that generalize the notion of cliques, important for applications such as community/module detection in various social and biological networks. However, the existing quasi-clique definition and algorithms are only applicable to undirected graphs. In this paper, we generalize the concept of quasi-cliques to directed graphs by proposing (γ1, γ2) -quasi-cliques which have density requirements in both inbound and outbound directions of each vertex in a quasi-clique subgraph. An efficient recursive algorithm is proposed to find maximal (γ1,γ2)-quasi-cliques which integrates many effective pruning rules that are validated by ablation studies. We also study the finding of top-k large quasi-cliques directly by bootstrapping the search from more compact quasi-cliques, to scale the mining to larger networks. The algorithms are parallelized with effective load balancing, and we demonstrate that they can scale up effectively with the number of CPU cores.

Recent studies on federated learning (FL) have sought to solve the system heterogeneity issue by designing customized local models for different clients. However, public dataset introduction, sensitive information exchange, non-trivial computational cost, or particular architecture requirement limit the applicability of most of them in real scenarios. This paper presents a novel federated fingerprint learning model for making full use of the computing power of each client with the customized local models for improving the FL convergence, while keeping the data and sensitive information safe and local. First, we decompose the parameters of each local model into two types of parameters: rigid ones that have fixed model architecture for ensuring the convergence of global model training and elastic ones that contain customized model structure and size for allowing to make full use of the computing power of each client based on individual data scale. Second, we adopt the standard FL scheme to update and aggregate the local rigid parameters. We introduce a Gaussian distribution as auxiliary input and output K local fingerprints respectively for the elastic parameters of all K local models. The server aggregates K local fingerprints into a global one and sends it back to the clients. A fingerprint-based aggregation strategy makes the local models indirectly receive the aggregated elastic parameters through the aggregation of K local fingerprints while fixing data locally. Last but not least, we design a parameter masking method to mask the rigid parameters irrelevant to the local classification task in the local models. We develop a parameter separation method to guarantee that the combination of unmasked rigid parameters in all local models are able to cover all the rigid parameters as many as possible, for further raising the utilization rate of each rigid parameter.

iPSCs are pluripotent stem cells generated from adult tissue through a process called cellular reprogramming. However, cellular reprogramming is a lengthy and inefficient process since only a small fraction of cells can reliably become iPSCs. The reprogramming efficiency is generally measured by counting the number of reprogrammed colonies that emerge and grow as rounded clusters of compact cells around 20 days after adding the reprogramming vectors. However, counting colonies manually is labor-intensive, time-consuming, and error-prone.This work develops a semi-automated tool for colony counting from iPSC culture plate images, where colonies are automatically annotated with their centers. Our model uses multi-task learning to jointly predict the colony centers and conduct colony segmentation, in hope that the latter will improve the performance of the former. An annotation tool is developed to facilitate the collection of ground-truth masks by crowdsourcing. Two center-based loss functions are investigated and compared, one based on oriented Gaussian kernel and the other based on average Hausdorff distance. Extensive experiments verify that (i) the former loss outperforms the latter, (ii) the segmentation head is effective in improving center predictions. Our code has been released at https://github.com/MTSami/iPSC-Colony-Counting.

Given raster imagery features and imperfect vector training labels with registration uncertainty, this paper studies a deep learning framework that can quantify and reduce the registration uncertainty of training labels as well as train neural network parameters simultaneously. The problem is important in broad applications such as streamline classification on Earth imagery or tissue segmentation on medical imagery, whereby annotating precise vector labels is expensive and time-consuming. However, the problem is challenging due to the gap between the vector representation of class labels and the raster representation of image features and the need for training neural networks with uncertain label locations. Existing research on uncertain training labels often focuses on uncertainty in label class semantics or characterizes label registration uncertainty at the pixel level (not contiguous vectors). To fill the gap, this paper proposes a novel learning framework that explicitly quantifies vector labels' registration uncertainty. We propose a registration-uncertainty-aware loss function and design an iterative uncertainty reduction algorithm by re-estimating the posterior of true vector label locations distribution based on a Gaussian process. Evaluations on real-world datasets in National Hydrography Dataset refinement show that the proposed approach significantly outperforms several baselines in the registration uncertainty estimations performance and classification performance.

Given earth imagery with spectral features on a terrain surface, this paper studies surface segmentation based on both explanatory features and surface topology. The problem is important in many spatial and spatiotemporal applications such as flood extent mapping in hydrology. The problem is uniquely challenging for several reasons: first, the size of earth imagery on a terrain surface is often much larger than the input of popular deep convolutional neural networks; second, there exists topological structure dependency between pixel classes on the surface, and such dependency can follow an unknown and non-linear distribution; third, there are often limited training labels. Existing methods for earth imagery segmentation often divide the imagery into patches and consider the elevation as an additional feature channel. These methods do not fully incorporate the spatial topological structural constraint within and across surface patches and thus often show poor results, especially when training labels are limited. Existing methods on semi-supervised and unsupervised learning for earth imagery often focus on learning representation without explicitly incorporating surface topology. In contrast, we propose a novel framework that explicitly models the topological skeleton of a terrain surface with a contour tree from computational topology, which is guided by the physical constraint (e.g., water flow direction on terrains). Our framework consists of two neural networks: a convolutional neural network (CNN) to learn spatial contextual features on a 2D image grid, and a graph neural network (GNN) to learn the statistical distribution of physics-guided spatial topological dependency on the contour tree. The two models are co-trained via variational EM. Evaluations on the real-world flood mapping datasets show that the proposed models outperform baseline methods in classification accuracy, especially when training labels are limited.

Network alignment, which aims at learning a matching between the same entities across multiple information networks, often suffers challenges from feature inconsistency, high-dimensional features, to unstable alignment results. This article presents a novel network alignment framework, Unsupervised Adversarial learning based Network Alignment(UANA), that combines generative adversarial network (GAN) and reinforcement learning (RL) techniques to tackle the above critical challenges. First, we propose a bidirectional adversarial network distribution matching model to perform the bidirectional cross-network alignment translations between two networks, such that the distributions of real and translated networks completely overlap together. In addition, two cross-network alignment translation cycles are constructed for training the unsupervised alignment without the need of prior alignment knowledge. Second, in order to address the feature inconsistency issue, we integrate a dual adversarial autoencoder module with an adversarial binary classification model together to project two copies of the same vertices with high-dimensional inconsistent features into the same low-dimensional embedding space. This facilitates the translations of the distributions of two networks in the adversarial network distribution matching model. Finally, we develop an RL based optimization approach to solve the vertex matching problem in the discrete space of the GAN model, i.e., directly select the vertices in target networks most relevant to the vertices in source networks, without unstable similarity computation that is sensitive to discriminative features and similarity metrics. Extensive evaluation on real-world graph datasets demonstrates the outstanding capability of UANA to address the unsupervised network alignment problem, in terms of both effectiveness and scalability.

Crowdsourcing is a hotspot research field which can facilitate machine learning by collecting labels to train models. Consequently, the state-of-the-art research efforts in crowdsourcing focus on truth inference or label integration, to remove inconsistent labels or to alleviate biased labeling. In turn, the integrated labels will be used to fine-tune machine learning models. Particularly, in this paper, we change the target of truth inference in crowdsourcing from discrete labels to multiple comments given by online participants, that is, the integration of the crowdsourced comments. For such a goal, we propose aSelf-play andSentiment-EmphasizedCommentIntegrationFramework (SSECIF), based on deepQ-learning, with three unique features. First, our framework SSECIF can generate the comment integration in a totally self-play way, without relying on the ground truth generated by human effort. Second, the integrated comment generated by SSECIF can include salient content with low redundancy. Third, the proposed framework SSECIF has emphasized, with a higher intensity, the sentiment in the integrated comment, in order to reflect the attitude or opinion more obviously. Extensive evaluation on real-world datasets demonstrates that SSECIF has achieved the best overall performance in terms of both effectiveness and efficiency, compared with the state-of-the-art methods.

Recently, linear regression models have shown to often produce rather competitive results against more sophisticated deep learning models. Meanwhile, the (weighted) matrix factorization approaches have been popular choices for recommendation in the past and widely adopted in the industry. In this work, we aim to theoretically understand the relationship between these two approaches, which are the cornerstones of model-based recommendations. Through the derivation and analysis of the closed-form solutions for two basic regression and matrix factorization approaches, we found these two approaches are indeed inherently related but also diverge in how they "scale-down" the singular values of the original user-item interaction matrix. We further introduce a new learning algorithm in searching (hyper)parameters for the closed-form solution and utilize it to discover the nearby models of the existing solutions. The experimental results demonstrate that the basic models and their closed-form solutions are indeed quite competitive against the state-of-the-art models, thus, confirming the validity of studying the basic models. The effectiveness of exploring the nearby models are also experimentally validated.

Recent studies have shown that graph learning models are highly vulnerable to adversarial attacks, and network alignment methods are no exception. How to enhance the robustness of network alignment against adversarial attacks remains an open research problem. In this paper, we propose a robust network alignment solution, RNA, for offering preemptive protection of existing network alignment algorithms, enhanced with the guidance of effective adversarial attacks. First, we analyze how popular iterative gradient-based adversarial attack techniques suffer from gradient vanishing issues and show a fake sense of attack effectiveness. Based on dynamical isometry theory, an attack signal scaling (ASS) method with established upper bound of feasible signal scaling is introduced to alleviate the gradient vanishing issues for effective adversarial attacks while maintaining the decision boundary of network alignment. Second, we develop an adversarial perturbation elimination (APE) model to neutralize adversarial nodes in vulnerable space to adversarial-free nodes in safe area, by integrating Dirac delta approximation (DDA) techniques and the LSTM models. Our proposed APE method is able to provide proactive protection to existing network alignment algorithms against adversarial attacks. The theoretical analysis demonstrates the existence of an optimal distribution for the APE model to reach a lower bound. Last but not least, extensive evaluation on real datasets presents that RNA is able to offer the preemptive protection to trained network alignment methods against three popular adversarial attack models.

Stock investment is a vehicle for many people to grow their wealth. However, market downturns can cause huge losses and need to be predicted for a timely sell. In fact, with effective prediction, stocks are a good investment even during periods of market volatility as many stocks are "on sale".News is an important source of signal for stock price movement. However, stock analysts usually adjust their analysis according to the news in a subject manner, and wrong judgments can cause investors huge losses.Twitter is a great source for breaking news, and provides a timely stream of signals on stock trends. News on Twitter also tends to have a great impact on the market due to the large number of Twitter users. This paper proposes a data-driven pipeline to timely incorporate Twitter news about a company into a time series prediction model on the company's stock price. Our approach, called BERT-LSTM (BELT), extracts informative features on stock price direction from Twitter news using the state-of-the-art natural language processing (NLP) model BERT, which are then used as covariates to a many-to-many stacked LSTM model that also utilizes historical stock prices to predict the direction of future stock price. Utilizing a carefully curated stock news dataset, we fine-tune BERT to effectively identify those news tweets that are relevant, and to extract NLP features that are indicative of price rises and falls. All model parameters are trained end-to-end to provide a data-driven and objective pipeline to incorporate news signals so as to avoid subjective analysis. Extensive experiments on real stock prices and Twitter news show that BELT is able to predict stock prices more accurately utilizing news information than if historical price data are used alone for prediction, and beats StockNet which is the current state of the art for news-based stock movement prediction.

Network alignment techniques, which aim to identify the same entities across multiple networks, often suffer challenges from feature inconsistency to transitivity law preservation. This paper presents a purely unsupervised network alignment method, KEMINA, with three original contributions. First, in order to address the feature inconsistency issue, an adversarial kernel embedding technique is proposed to extract network-invariant information among multiple networks without prior alignment knowledge, and project them into the common embedding space. Second, a multinomial generative adversarial network (GAN) model is developed to train multiple network alignment tasks simultaneously in an unsupervised manner with preserving the transitivity law property. Third but last, a variational inference model is designed to alleviate the data sparsity and inadequate training issues by filling realistic detail for vertices with sparse features and generating real-looking supplementary vertex samples within limited training opportunity of each pair of source and target networks.

Recent studies have shown that graph mining models are vulnerable to adversarial attacks. This paper proposes a robust meta network embedding framework, RoMNE, which improves the robustness of multiple network embedding on adversarial noisy networks while preserving the utility on original clean ones. First, we propose a generic meta learning based multiple network embedding model that can quickly adapt it to new embedding tasks on a variety of network data with only a small number of parameter and training updates. Second, Gumbel estimator and Gaussian smoothing techniques are introduced to implement differentiable approximation for optimizing non-differential objective of effective adversarial attacks. Last but not least, the adversarial attack and defense models are integrated into a dynamic adversarial training model. The competition of two models helps the latter be robust to adversarial attacks.

Network embedding techniques aim to encode each vertex/edge as a low-dimensional vector, enabling easy integration with existing graph mining algorithms. This paper presents a novel network embedding framework, VEEMBEDCLASS, that combines local vertex/edge embedding with deep matrix fusion and Siamese multi-label classification for facilitating classification-based local network embedding. First, we propose to perform the embeddings of each vertex/edge on K local vertex/edge embedding models respectively, with the joint optimization by considering both intra-class and inter-class correlations, to learn their latent local features on each class. The deep matrix fusion technique is developed to preserve the first-order and second-order proximity of vertices and edges on each of K classes simultaneously. Second, a Student t-distribution based Siamese multi-label classification method is designed to train associated vertices and edges with similar local characteristics together and learn their class membership probabilities, in response to the power-law vertex degree distribution widespread in real graphs. A principle of vertex-edge homophily is introduced to guarantee that the common edge/vertex shared by two associated vertices/edges and themselves are similar in terms of both structural correlations and class memberships. Finally, we integrate local vertex/edge embedding and Siamese multi-label classification into a unified model by mutually enhancing each other.

Real-world graphs are usually very sparse in terms of inadequate edges and labels as well as have poor quality due to a large amount of noisy data. In this paper, we propose a classification-based local vertex ranking architecture through dual generative adversarial networks in the semi-supervised setting, DQGAN, for analyzing sparse noisy graphs with rarely labeled data. First, we develop a quadruple generative adversarial ClassNet model to address the noisy data and data sparsity issues as well as to classify each vertex into K classes by automatically creating imaginary/real-looking supplementary labeled vertices with the quite different/similar distributions as real vertices, without the high cost of multi-step graph propagation, heterogeneous graph mining, and iterative weight learning. In addition, the vertex label vicinity is incorporated into the classification model to capture the pairwise vertex closeness based on the labeling and align the vertex label vicinity with the well-known vertex homophily for preserving the original structural semantics in the classification space. Second, we present a quintuple generative adversarial RankNet framework to locally rank each vertex on each of K classes by designing the game of multiple competitors utilizing the mix of real and noisy data to fight against each other, for improving the robustness of local vertex ranking to noisy data with few help from human efforts. The cycle ranking consistency strategy is designed to make the ranking quality verifiable through the bidirectional information-lossless translations between the original features and the ranking features. We propose to utilize the relaxed local PageRank property to produce high-quality local vertex ranking results in the context of information networks. Third but last, extensive evaluation on real graph datasets demonstrates that DQGAN outperforms existing representative methods in terms of both classification and ranking in the semi-supervised setting.

Network alignment, which aims to learn a matching between the same entities across multiple information networks, often suffers challenges from feature inconsistency, high-dimensional features, to unstable alignment results. This paper presents a novel network alignment framework, RANA, that combines dual generative adversarial network (GAN) techniques to match the distributions of two networks based on two dimensions of distance and shape. First, we propose an adversarial network distribution matching model to perform the bidirectional cross-network alignment translations between two networks, such that the cross-network transformed distributions of two networks move closer to each other and finally meet with each other halfway. In addition, a homophily consistency loss is introduced to maintain the vertex homophily consistency between pairwise vertices on two networks in both the embedding space. Second, in order to address the feature inconsistency issue, we integrate a dual adversarial autoencoder module with an adversarial two-class classification model together to twist the cross-network transformed distributions of two networks, such that two distributions could have the same shape. This facilitates the translations of the distributions of two networks in the adversarial network distribution matching model. Moreover, a semantic preservation loss is introduced to preserve the original embedding semantics of one network when this network is translated to another network and returned to itself. Third but last, the competition game by integrating the above two adversarial models together can help project two copies of the same vertices with high-dimensional inconsistent features into the same low-dimensional embedding space, and thus guarantee the distribution consistency between two networks in terms of both distance and shape.

Network embedding aims to learn latent low dimensional representation of vertices in graphs while preserving the intrinsic characteristics of graph data. In this paper, we propose a density-aware local autoencoder embedding architecture, DAL-SAE, with three features. First, we develop a flexible density-aware local deep autoencoder embedding method to perform local embedding on each of K clustering-based subgraphs with the optimization at both vertex and subgraph levels, in response to imbalanced density-based local characteristics of vertices and subgraphs. We design K local autoencoder embedding models, each with individual parameters and structure, to jointly train K subgraphs and optimize the loss functions within and across clusters. Second, we design an autoencoder graph clustering method to optimize local embedding and graph clustering simultaneously and capture local, clustering, and global network structure in the learning process. Third but last, a density-aware local Siamese autoencoder embedding approach can be utilized to train multiple clustering-based subgraphs with similar local characteristics on the common Siamese networks, to save the memory consumption of multiple local embedding models as well as maintain the similar embedding features.

Traditional network representation learning techniques aim to learn latent low-dimensional representation of vertices in graphs. This paper presents a novel edge representation learning framework, GANDLERL, that combines generative adversarial network based multi-label classification with density-adaptive local edge representation learning for producing high-quality low-dimensional edge representations. First, we design a generative adversarial network based multi-label edge classification model to classify rarely labeled edges in graphs with a large amount of noise data into K classes. A four-player zero-sum game model, with the mixed training of true and real-looking fake edges as well as a contrastive loss containing a similar-loss and a dissimilar-loss, is proposed to improve the classification quality of unlabeled edges. Second, a local autoencoder edge representation learning method is developed to design K local representation learning models, each with individual parameters and structure to perform local representation learning on each of K classification-based subgraphs with unique local characteristics and jointly optimize the loss functions within and across classes. Third but last, we propose a density-adaptive edge representation learning method with the optimization at both edge and subgraph levels to address the representation learning of graph data with highly imbalanced vertex degree and edge distribution.

The cloud service marketplace (CSM) is an exploratory project aiming to provide “an AppStore for Services.” It is an intelligent online marketplace that facilitates service discovery and acquisition for enterprise customers. Traditional service discovery and acquisition are time-consuming. In the era of OneClick Checkout and pay-as-you-go service plans, users expect services to be purchased online efficiently and conveniently. However, as services are complex and different from software apps, the currently prevailing App Store based on keyword search is inadequate for services. In CSM, exploring and configuring services are an iterative process. Customers provide their requirements in natural language and interact with the system through questioning and answering. Learning from the input, the system can incrementally clarify users’ intention, narrow down the candidate services, and profile the configuration information for the candidates at the same time. CSM’s back end is built around the Services Knowledge Graph (SKG) and leverages data mining technologies to enable the semantic understanding of customers’ requirements. To quantitatively assess the value of CSM, empirical evaluation on real and synthetic datasets and case studies are given to demonstrate the efficacy and effectiveness of the proposed system.

Meta paths are good mechanisms to improve the quality of graph analysis on heterogeneous information networks. This paper presents a meta path graph clustering framework, VEPATHCLUSTER, that combines meta path vertex-centric clustering with meta path edge-centric clustering for improving the clustering quality of heterogeneous networks. First, we propose an edge-centric path graph model to capture the meta-path dependencies between pairwise path edges. We model a heterogeneous network containing M types of meta paths as M vertex-centric path graphs and M edge-centric path graphs. Second, we propose a clustering-based multigraph model to capture the fine-grained clustering-based relationships between pairwise vertices and between pairwise path edges. We perform clustering analysis on both a unified vertex-centric path graph and each edge-centric path graph to generate vertex clustering and edge clusterings of the original heterogeneous network respectively. Third, a reinforcement algorithm is provided to tightly integrate vertex-centric clustering and edge-centric clustering by mutually enhancing each other. Finally, an iterative learning strategy is presented to dynamically refine both vertex-centric clustering and edge-centric clustering by continuously learning the contributions and adjusting the weights of different path graphs.

Large-scale real-world graphs are known to have highly skewed vertex degree distribution and highly skewed edge weight distribution. Existing vertex-centric iterative graph computation models suffer from a number of serious problems: (1) poor performance of parallel execution due to inherent workload imbalance at vertex level; (2) inefficient CPU resource utilization due to short execution time for low-degree vertices compared to the cost of in-memory or on-disk vertex access; and (3) incapability of pruning insignificant vertices or edges to improve the computational performance. In this paper, we address the above technical challenges by designing and implementing a scalable, efficient, and provably correct two-tier graph parallel processing system, GraphTwist. At storage and access tier, GraphTwist maximizes parallel efficiency by employing three graph parallel abstractions for partitioning a big graph by slice, strip or dice based partitioning techniques. At computation tier, GraphTwist presents two utility-aware pruning strategies: slice pruning and cut pruning, to further improve the computational performance while preserving the computational utility defined by graph applications. Theoretic analysis is provided to quantitatively prove that iterative graph computations powered by utility-aware pruning techniques can achieve a very good approximation with bounds on the introduced error.

Social influence analysis has shown great potential for strategic marketing decision. It is well known that people influence one another based on both their social connections and the social activities that they have engaged in the past. In this article, we develop an innovative and high-performance social influence based graph clustering framework with four unique features. First, we explicitly distinguish social connection based influence (self-influence) and social activity based influence (co-influence). We compute the self-influence similarity between two members based on their social connections within a single collaboration network, and compute the co-influence similarity by taking into account not only the set of activities that people participate but also the semantic association between these activities. Second, we define the concept of influence-based similarity by introducing a unified influence-based similarity matrix that employs an iterative weight update method to integrate self-influence and co-influence similarities. Third, we design a dynamic learning algorithm, called SI-C luster , for social influence based graph clustering. It iteratively partitions a large social collaboration network into K clusters based on both the social network itself and the multiple associated activity information networks, each representing a category of activities that people have engaged. To make the SI-C luster algorithm converge fast, we transform sophisticated nonlinear fractional programming problem with respect to multiple weights into a straightforward nonlinear parametric programming problem of single variable. Finally, we develop an optimization technique of diagonalizable-matrix approximation to speed up the computation of self-influence similarity and co-influence similarities. Our SI-Cluster-Opt significantly improves the efficiency of SI-Cluster on large graphs while maintaining high quality of clustering results. Extensive experimental evaluation on three real-world graphs shows that, compared to existing representative graph clustering algorithms, our SI-C luster -O pt approach not only achieves a very good balance between self-influence and co-influence similarities but also scales extremely well for clustering large graphs in terms of time complexity while meeting the guarantee of high density, low entropy and low Davies--Bouldin Index.

Distributed service networks are popular platforms for service providers to offer services to consumers and for service consumers to acquire services from unknown parties. eBay and Amazon are two well-known examples of enabling and hosting such service networks to connect service providers to service consumers. Trust management is a critical component for scaling such distributed service networks to a large and growing number of participants. In this article, we present ServiceTrust ++ , a feedback quality--sensitive and attack resilient trust management scheme for empowering distributed service networks with effective trust management capability. Compared with existing trust models, ServiceTrust ++ has several novel features. First, we present six attack models to capture both independent and colluding attacks with malicious cliques, malicious spies, and malicious camouflages. Second, we aggregate the feedback ratings based on the variances of participants’ feedback behaviors and incorporate feedback similarity as weight into the local trust algorithm. Third, we compute the global trust of a participant by employing conditional trust propagation based on the feedback similarity threshold. This allows ServiceTrust ++ to control and prevent malicious spies and malicious camouflage peers from boosting their global trust scores by manipulating the feedback ratings of good peers and by taking advantage of the uniform trust propagation. Finally, we systematically combine a trust-decaying strategy with a threshold value--based conditional trust propagation to further strengthen the robustness of our global trust computation against sophisticated malicious feedback. Experimental evaluation with both simulation-based networks and real network dataset Epinion show that ServiceTrust ++ is highly resilient against all six attack models and highly effective compared to EigenTrust, the most popular and representative trust propagation model to date.

Multi-label classification of heterogeneous information networks has received renewed attention in social network analysis. In this paper, we present an activity-edge centric multi-label classification framework for analyzing heterogeneous information networks with three unique features. First, we model a heterogeneous information network in terms of a collaboration graph and multiple associated activity graphs. We introduce a novel concept of vertex-edge homophily in terms of both vertex labels and edge labels and transform a general collaboration graph into an activity-based collaboration multigraph by augmenting its edges with class labels from each activity graph through activity-based edge classification. Second, we utilize the label vicinity to capture the pairwise vertex closeness based on the labeling on the activity-based collaboration multigraph. We incorporate both the structure affinity and the label vicinity into a unified classifier to speed up the classification convergence. Third, we design an iterative learning algorithm, AEClass, to dynamically refine the classification result by continuously adjusting the weights on different activity-based edge classification schemes from multiple activity graphs, while constantly learning the contribution of the structure affinity and the label vicinity in the unified classifier. Extensive evaluation on real datasets demonstrates that AEClass outperforms existing representative methods in terms of both effectiveness and efficiency.

Social networks continue to grow in size and the type of information hosted. We witness a growing interest in clustering a social network of people based on both their social relationships and their participations in activity based information networks. In this paper, we present a social influence based clustering framework for analyzing heterogeneous information networks with three unique features. First, we introduce a novel social influence based vertex similarity metric in terms of both self-influence similarity and co-influence similarity. We compute self-influence and co-influence based similarity based on social graph and its associated activity graphs and influence graphs respectively. Second, we compute the combined social influence based similarity between each pair of vertices by unifying the self-similarity and multiple co-influence similarity scores through a weight function with an iterative update method. Third, we design an iterative learning algorithm, SI-Cluster, to dynamically refine the K clusters by continuously quantifying and adjusting the weights on self-influence similarity and on multiple co-influence similarity scores towards the clustering convergence. To make SI-Cluster converge fast, we transformed a sophisticated nonlinear fractional programming problem of multiple weights into a straightforward nonlinear parametric programming problem of single variable. Our experiment results show that SI-Cluster not only achieves a better balance between self-influence and co-influence similarities but also scales extremely well for large graph clustering.

Social networks, sensor networks, biological networks, and many other information networks can be modeled as a large graph. Graph vertices represent entities, and graph edges represent their relationships or interactions. In many large graphs, there is usually one or more attributes associated with every graph vertex to describe its properties. In many application domains, graph clustering techniques are very useful for detecting densely connected groups in a large graph as well as for understanding and visualizing a large graph. The goal of graph clustering is to partition vertices in a large graph into different clusters based on various criteria such as vertex connectivity or neighborhood similarity. Many existing graph clustering methods mainly focus on the topological structure for clustering, but largely ignore the vertex properties, which are often heterogenous. In this article, we propose a novel graph clustering algorithm, SA-Cluster , which achieves a good balance between structural and attribute similarities through a unified distance measure. Our method partitions a large graph associated with attributes into k clusters so that each cluster contains a densely connected subgraph with homogeneous attribute values. An effective method is proposed to automatically learn the degree of contributions of structural similarity and attribute similarity. Theoretical analysis is provided to show that SA-Cluster is converging quickly through iterative cluster refinement. Some optimization techniques on matrix computation are proposed to further improve the efficiency of SA-Cluster on large graphs. Extensive experimental results demonstrate the effectiveness of SA-Cluster through comparisons with the state-of-the-art graph clustering and summarization methods.

In recent years, many networks have become available for analysis, including social networks, sensor networks, biological networks, etc. Graph clustering has shown its effectiveness in analyzing and visualizing large networks. The goal of graph clustering is to partition vertices in a large graph into clusters based on various criteria such as vertex connectivity or neighborhood similarity. Many existing graph clustering methods mainly focus on the topological structures, but largely ignore the vertex properties which are often heterogeneous. Recently, a new graph clustering algorithm, SA-Cluster, has been proposed which combines structural and attribute similarities through a unified distance measure. SA-Cluster performs matrix multiplication to calculate the random walk distances between graph vertices. As the edge weights are iteratively adjusted to balance the importance between structural and attribute similarities, matrix multiplication is repeated in each iteration of the clustering process to recalculate the random walk distances which are affected by the edge weight update. In order to improve the efficiency and scalability of SA-Cluster, in this paper, we propose an efficient algorithm Inc-Cluster to incrementally update the random walk distances given the edge weight increments. Complexity analysis is provided to estimate how much runtime cost Inc-Cluster can save. Experimental results demonstrate that Inc-Cluster achieves significant speedup over SA-Cluster on large graphs, while achieving exactly the same clustering quality in terms of intra-cluster structural cohesiveness and attribute value homogeneity.

The goal of graph clustering is to partition vertices in a large graph into different clusters based on various criteria such as vertex connectivity or neighborhood similarity. Graph clustering techniques are very useful for detecting densely connected groups in a large graph. Many existing graph clustering methods mainly focus on the topological structure for clustering, but largely ignore the vertex properties which are often heterogenous. In this paper, we propose a novel graph clustering algorithm, SA-Cluster , based on both structural and attribute similarities through a unified distance measure. Our method partitions a large graph associated with attributes into k clusters so that each cluster contains a densely connected subgraph with homogeneous attribute values. An effective method is proposed to automatically learn the degree of contributions of structural similarity and attribute similarity. Theoretical analysis is provided to show that SA-Cluster is converging. Extensive experimental results demonstrate the effectiveness of SA-Cluster through comparison with the state-of-the-art graph clustering and summarization methods.