Graph neural networks (GNNs) have become a dominant modeling paradigm for graph-structured data, and the emergence of large language models (LLMs) has spurred growing interest in integrating external semantic knowledge into GNNs. Current LLM-based GNNs are devoted to extracting semantically similar information from LLMs to enhance representation learning. However, they generally overlook key signals that are semantically dissimilar but exhibit stronger inter-class discriminative ability. Especially when the original graph data contains noise or semantic ambiguity, a single similarity-based semantic augmentation strategy not only fails to provide effective enhancement, but may also amplify misleading signals generated by the LLM in response to low-quality inputs or its own hallucinations, further degrading the discriminative power and robustness of GNNs. To this end, we propose a dual positive-negative knowledge extraction strategy based on LLMs, and integrate it with a knowledge distillation mechanism to dynamically transfer multi-dimensional enhanced signals to GNNs, thereby achieving fine-grained and robust graph representation learning. Specifically, we design personalized prompts to guide LLMs in generating semantically similar positive signals and semantically dissimilar negative signals, which help the model capture intra-class consistency and inter-class distinction. Then, we further generate structural and semantic reasoning as supplementary knowledge to support the rationality and guidance of supervision signals. To identify high-confidence transferred knowledge, we introduce a language-based evaluation mechanism to filter low-confidence or hallucinated outputs. Finally, under a unified distillation framework, our method uses both positive and negative knowledge to guide GNN training, achieving adaptive and robust representation learning. Extensive experiments on benchmark datasets verify the superior performance of our approach across various tasks.

Heterogeneous graphs are widely used to model real-world systems with diverse entity types and relational structures, and existing methods have shown promising performance in various applications. However, most current models assume balanced and semantically aligned features across nodes, which rarely holds in practice. In scenarios such as social risk governance, node types often exhibit severe feature imbalance, making it difficult for standard aggregation mechanisms to extract meaningful signals. This imbalance leads to three key challenges: inaccurate neighbor weighting, noise propagation, and biased representations skewed toward text-rich nodes. To address these issues, we propose HeCoGNN, a collaborative and adaptive aggregation framework that jointly performs neighbor filtering and relation-aware message calibration, enabling robust representation learning under semantic disparity. Experiments on real-world social governance graphs show that HeCoGNN consistently outperforms state-of-the-art baselines, particularly in handling underrepresented and noisy node types.

Universal graph pre-training has emerged as a key paradigm in graph representation learning, offering a promising way to train encoders to learn transferable representations from unlabeled graphs and to effectively generalize across a wide range of downstream tasks. However, recent explorations in universal graph pre-training primarily focus on homogeneous graphs and it remains unexplored for heterogeneous graphs, which exhibit greater structural and semantic complexity. This heterogeneity makes it highly challenging to train a universal encoder for diverse heterogeneous graphs: (i) the diverse types with dataset-specific semantics hinder the construction of a unified representation space; (ii) the number and semantics of meta-paths vary across datasets, making encoding and aggregation patterns learned from one dataset difficult to apply to others. To address these challenges, we propose a novel Meta-path-aware Universal heterogeneous Graph pre-training (MUG) approach. Specifically, for challenge (i), MUG introduces a input unification module that integrates information from multiple node and relation types within each heterogeneous graph into a unified representation. This representation is then projected into a shared space by a dimension-aware encoder, enabling alignment across graphs with diverse schemas. Furthermore, for challenge (ii), MUG trains a shared encoder to capture consistent structural patterns across diverse meta-path views rather than relying on dataset-specific aggregation strategies, while a global objective encourages discriminability and reduces dataset-specific biases. Extensive experiments demonstrate the effectiveness of MUG on some real datasets.

Real-world heterogeneous data is commonly modeled as heterogeneous information networks (HINs). Building upon advancements in graph neural networks (GNNs), existing research has significantly progressed in semi-supervised and self-supervised paradigms for heterogeneous GNNs (HGNNs). However, these methods overlook inherent structural deficiencies in raw heterogeneous graphs. We identifies unique structural noise in HINs: missing potential critical edges and multi-relational semantically redundant edges, which force existing HGNNs to learn suboptimal representations on fixed topologies. Crucially, prior limited studies address only partial noise while remaining architecturally entrenched and tightly coupled with specific models. To break this bottleneck, we propose a plug-and-play Heterogeneous graph Structure ADaPter (HSADP) that simultaneously resolves task/model decoupling challenges while accounting for HIN-specific structural properties with with two core components: a dynamic homogeneous subgraph enhancer recovering latent topology across semantic views and a learnable heterogeneous edge discriminator dynamically suppressing redundant edges while collaboratively optimizing semantic graphs. Extensive experiments across multi-domain datasets demonstrate our method’s effectiveness and compatibility. The adapter significantly boosts node classification accuracy for multiple SOTA approaches and surpasses specially designed heterogeneous graph structure learning models.

Defending against backdoor attacks on graphs has become increasingly critical. Existing methods predominantly focus on detecting and removing triggers by identifying inconsistencies between trigger and clean nodes. However, adversaries can design triggers that closely resemble clean nodes, making them challenging to detect. Therefore, understanding the mechanisms underlying backdoor attacks is crucial. In this work, we observe an interesting phenomenon: in backdoored models, specific ''backdoor neurons'' (embedding dimensions) are more likely to be activated, causing nodes to be misclassified to the target label. This is largely due to the graph structure, where malicious information propagates through node neighborhoods, activating specific neurons and target label. Based on this observation, we theoretically and empirically demonstrate how graph backdoor attacks exploit this propagation mechanism to effectively poison the target node's embedding. Meanwhile, we propose a novel defense called Graph Backdoor Neuron Defense (GBND) to identify, unlearn, and recover backdoor neurons. Specifically, we design a novel reverse engineering technique to identify triggers that activate backdoor neurons, and eliminate their harmful effects by asymmetric unlearning and recovering at the neuron level. Extensive experiments on four datasets validate the effectiveness of GBND in defending against backdoor attacks.

Recent advances in generic large models, such as GPT and DeepSeek, have motivated the introduction of universality to graph pre-training, aiming to learn rich and generalizable knowledge across diverse domains using graph representations to improve performance in various downstream applications. However, most existing methods face challenges in learning effective knowledge from generic graphs, primarily due to simplistic data alignment and limited training guidance. The issue of simplistic data alignment arises from the use of a straightforward unification for highly diverse graph data, which fails to align semantics and misleads pre-training models. The problem with limited training guidance lies in the arbitrary application of in-domain pre-training paradigms to cross-domain scenarios. While it is effective in enhancing discriminative representation in one data space, it struggles to capture effective knowledge from many graphs. To address these challenges, we propose a novel Latent sEmantic Distribution Alignment (LEDA) model for universal graph pre-training. Specifically, we first introduce a dimension projection unit to adaptively align diverse domain features into a shared semantic space with minimal information loss. Furthermore, we design a variational semantic inference module to obtain the shared latent distribution. The distribution is then adopted to guide the domain projection, aligning it with shared semantics across domains and ensuring cross-domain semantic learning. LEDA exhibits strong performance across a broad range of graphs and downstream tasks. Remarkably, in few-shot cross-domain settings, it significantly outperforms in-domain baselines and advanced universal pre-training models.

Recently, Graph Foundation Models (GFMs) have emerged as a central focus in the field of graph learning due to their strong generalizability to various unseen graphs. However, existing GFMs typically work under the homophily assumption, and the exploration of universality on heterophilic graphs is still in its early stages. In fact, even in homophilic graphs, there exists limited yet informative heterophilic information that is not fully exploited by current GFMs. Moreover, due to the requirement for universality, the heterophily issue faced by GFMs is more challenging than in classical graph learning, as it requires training a single model to adapt to varying structures, features, and tasks. Classic heterophilic graph learning methods primarily based on the node-level homophily or heterophily. However, we highlight that homophily and heterophily exist not only at the node semantic level, but also at a finer granularity across individual feature dimensions. This finding enables GFMs to adapt to heterophilic graphs and better utilize the small amount of heterophilic information in homophilic graphs. Based on this, we propose Topology-aware Feature Sorting Graph Foundation Model (TFSGFM), which employs a feature-level topology-aware sorting strategy and a dual-channel graph neural network framework, enabling unified modeling of both feature and structure. Extensive experiments demonstrate the strong generalizability of TFSGFM. The source code is available at https://github.com/hedongxiao-tju/TFSGFM.

Graph-based fraud detection (GFD) aims to identify fraud nodes within graph-structured data that significantly deviate from the majority of benign nodes. However, existing graph neural networks (GNNs) often struggle in GFD scenarios due to their reliance on homophily assumption, which is frequently violated by the inherent homophily-heterophily mixture of fraud graphs. Moreover, most methods focus primarily on local topology, overlooking mesoscopic community structures, making them less efficient in detecting suspicious patterns like densely connected subgraphs. To address the aforementioned issues, we present NeCo, a novel approach that integrates mixture of neighborhood and community experts for graph-based fraud detection. Specifically, we first introduce a fraud-discriminative representation preservation mechanism from a neighborhood perspective, leveraging the empirical finding that fraud nodes tend to exhibit larger feature propagation discrepancies compared to benign nodes. We then design a community-oriented node representation module that models structural compactness among nodes, enabling the detection of suspicious topological patterns associated with fraud behaviors. By integrating these two complementary perspectives, NeCo can effectively captures both local inconsistency and global structural irregularity. Extensive experiments across five real-world datasets demonstrate the effectiveness of our proposed NeCo over state-of-the-art baselines.

The widespread proliferation of fake news on the Internet, especially in multi-modal formats, poses a substantial threat to society. Most deep learning-based approaches for fake news detection yield accurate predictions but lack explainability. Existing models focusing on explainability visualize key components from results or generate surface causes via Large Language Models. However, they can hardly provide the deep rationale behind the fabrication of fake news, which is indispensable for misinformation mitigation. Thus, we approach explainability from a different perspective, focusing on explaining how fake news is fabricated, which we term deceptive patterns, at its very source. First, four types of deceptive patterns are pre-established, namely Image Manipulation, Cross-modal Inconsistency, Image Repurposing and Others. Based on this, we propose GE-NSLM, a General Explainable Neuro-Symbolic Latent Model that integrates the power of Large Vision Language Models, which not only provides accurate judgments but also offers insights on deceptive patterns. Specifically, each deceptive pattern is represented as a binary learnable latent variable, obtained through amortized variational inference and weak supervision guided by logical rules. Experiments show GE-NSLM achieves competitive performance. More importantly, it provides interpretable insights into the underlying reasons why specific news items are fake.

The cold-start recommendation has been challenging due to the limited historical interactions for new users and new items. Recently, methods based on meta-learning and graph neural networks are effective on this problem. However, these methods mainly focus on the missing user–item interactions in cold-start scenarios, overlooking the missing of user/item feature information, which significantly limits the quality and effectiveness of node embeddings. To address this problem, we propose an innovative Large Language Model-Guided Feature-Structure Augmented Graph Neural Network (LLM-FSGNN). The proposed framework integrates external semantic knowledge with internal graph structures via LLMs. It generates more comprehensive and robust user and item node representations. Specifically, we leverage the semantic reasoning capabilities of LLMs to extract textual descriptions of users and items, constructing a semantic view to enhance ambiguous or missing attributes and enrich node feature representations. Simultaneously, by analyzing item content to better understand user preferences, LLMs can accurately predict users’ potential intentions toward items and uncover latent interaction relationships, thus strengthening structural features. In addition, we introduce a graph structure enhancement module to complement structural relations from a graph-based perspective, mitigating hallucination issues in LLMs. Experimental results on multiple public benchmark datasets demonstrate consistent improvements in cold-start scenarios. For example, LLM-FSGNN achieves 4.8% lower MAE on MovieLens100K, 3.6% lower MAE on MovieLens1M, and 4.2% lower MAE on Amazon compared with the current state-of-the-art methods.

Rumor detection is critical as the spread of misinformation on social media threatens social stability. The propagation structure has garnered attention for its ability to capture discriminative information, such as crowd stance, which has led to the development of enhanced detection methods. However, these detectors are vulnerable to attacks that can manipulate results and evade detection, potentially disrupting public order or influencing public opinion. While adversarial attacks on rumor detectors have been studied, the use of backdoor attacks—an evasive and powerful method—remains unexplored due to the challenges in applying them to propagation trees. In this paper, we introduce the first backdoor attack framework against propagation-based rumor detectors, designed to maintain overall detector performance while enabling targeted attacks on specific rumors. We propose an adaptive discrete trigger generator that injects trigger nodes into critical nodes, creating evasive, transferable attacks. Extensive experiments on three real-world rumor datasets demonstrate that our framework effectively undermines the performance of propagation-based rumor detectors and is transferable across different architectures.

Knowledge Graphs (KGs) are structured data presented as directed graphs. Due to the common issues of incompleteness and inaccuracy encountered during construction and maintenance, completing KGs becomes a critical task. Inductive Knowledge Graph Completion (KGC) excels at inferring patterns or models from seen data to be applied to unseen data. However, existing methods mainly focus on new entities, while relations are usually randomly initialized. To this end, we propose TARGI, a simple yet effective inductive method for KGC. Specifically, we first construct a global relation graph for each topology from a global graph perspective, thus leveraging the in-variance of relation structures. We then utilize this graph to aggregate the rich embeddings of new relations and new entities, thereby performing KGC robustly in inductive scenarios. This successfully addresses the excessive reliance on the degree of relations and resolves the high complexity and limited scope of enclosing subgraph sampling in existing fully inductive algorithms. We conduct KGC experiments on six inductive datasets using inference data where entities are entirely new and new relations at 100 percent, 50 percent, and 0 percent radios. Extensive results demonstrate that our model accurately learns the topological structures and embeddings of new relations, and guides the embedding learning of new entities. Notably, our model outperforms 15 SOTA methods, especially in two fully inductive datasets.

Graph Contrastive Learning (GCL) aims to self-supervised learn low-dimensional graph representations, primarily through instance discrimination, which involves manually mining positive and negative pairs from graphs, increasing the similarity of positive pairs while decreasing negative pairs. Drawing from the success of Contrastive Learning (CL) in other domains, a consensus has been reached that the effectiveness of GCLs depends on a large number of negative pairs. As a result, despite the significant computational overhead, GCLs typically leverage as many negative node pairs as possible to improve model performance. However, given that nodes within a graph are interconnected, we argue that nodes cannot be treated as independent instances. Therefore, we challenge this consensus: Does employing more negative nodes lead to a more effective GCL model? To answer this, we explore the role of negative nodes in the commonly used InfoNCE loss for GCL and observe that: (1) Counterintuitively, a large number of negative nodes can actually hinder the model's ability to distinguish between nodes with different semantics. (2) A smaller number of high-quality and non-topologically coupled negative nodes are sufficient to enhance the discriminability of representations. Based on these findings, we propose a new method called GCL with Effective and Efficient Negative samples, E2Neg, which learns discriminative representations using only a very small set of representative negative samples. E2Neg significantly reduces computational overhead and speeds up model training. We demonstrate the effectiveness and efficiency of E2Neg across multiple datasets compared to other GCL methods.

The cold-start recommendation has been challenging due to the limited historical interactions for new users and new items. Recently, methods based on meta-learning and graph neural networks have been effective in this problem. However, these methods mainly focus on the missing user-item interactions in cold-start scenarios, overlooking the missing of user/item feature information, which significantly limits the quality and effectiveness of node embeddings. To address this issue, we propose a new method called Feature-Structure Adaptive Completion Graph Neural Network (FS-GNN), which is designed to tackle the cold-start problem by simultaneously addressing the missing feature and structure information in a bipartite graph composed of users and items. Specifically, we first design a trainable feature completion module that leverages the knowledge emergence abilities of large language models to enhance node embedding and mitigate the impact of missing features. Then, we incorporate a three-channel structure completion module to simultaneously complete the structures among users-users, items-items, as well as users-items. Finally, we adaptively integrate the feature and structure completion modules in an end-to-end fashion, so as to minimize cross-module interference when completing features and structures simultaneously. This generates more comprehensive and robust embeddings for users and items in recommendation tasks. Experimental results on multiple public benchmark datasets demonstrate significant improvements in our proposed FS-GNN in cold-start scenarios, outperforming or being competitive with state-of-the-art methods.

Interactive Recommendation (IR) has gained significant attention recently for its capability to quickly capture dynamic interest and optimize both short and long term objectives. IR agents are typically implemented through Deep Reinforcement Learning (DRL), because DRL is inherently compatible with the dynamic nature of IR. However, DRL is currently not perfect for IR. Due to the large action space and sample inefficiency problem, training DRL recommender agents is challenging. The key point is that useful features cannot be extracted as high-quality representations for the recommender agent to optimize its policy. To tackle this problem, we propose Contrastive Representation for Interactive Recommendation (CRIR). CRIR efficiently extracts latent, high-level preference ranking features from explicit interaction, and leverages the features to enhance users’ representation. Specifically, the CRIR provides representation through one representation network, and refines it through our proposed Preference Ranking Contrastive Learning (PRCL). The key insight of PRCL is that it can perform contrastive learning without relying on computations involving high-level representations or large potential action sets. Furthermore, we also propose a data exploiting mechanism and an agent training mechanism to better adapt CRIR to the DRL backbone. Extensive experiments have been carried out to show our method's superior improvement on the sample efficiency while training an DRL-based IR agent.

Graph Neural Networks (GNNs) have recently achieved significant success in several graph-related tasks. However, traditional GNNs and their variants are constantly limited by the implicit homophily, assuming neighboring nodes belong to the same class. This results in weak performance on heterophilic graphs where most nodes are linked to neighbors of different classes. Despite the numerous attempts to adequately deal with heterophily, most methods still use the uniform propagation aggregation mechanism. In this paper, we argue that identifying neighbors with different class labels and exploiting them individually is crucial for heterophilic GNNs. We then propose a simple and efficient novel co-training approach, EG-GCN, which uses group aggregation to handle homophilic and heterophilic neighbors separately. In EG-GCN, we first use an edge discriminator to classify edges and split the neighborhood of every node into two parts. We then apply group graph convolution to the divided neighborhoods to obtain node representations. During training, we continuously optimize the edge discriminator to improve neighborhood partition and use the node classification results to identify highly confident unlabeled nodes to expand the edge training set. This co-training strategy enables both components to enhance each other mutually. Extensive experiments demonstrate that EG-GCN significantly outperforms the state-of-the-art approaches.

Multimodal Sentiment Analysis (MSA) stands as a critical research frontier, seeking to comprehensively unravel human emotions by amalgamating text, audio, and visual data. Yet, discerning subtle emotional nuances within audio and video expressions poses a formidable challenge, particularly when emotional polarities across various segments appear similar. In this paper, our objective is to spotlight emotion-relevant attributes of audio and visual modalities to facilitate multimodal fusion in the context of nuanced emotional shifts in visual-audio scenarios. To this end, we introduce DEVA, a progressive fusion framework founded on textual sentiment descriptions aimed at accentuating emotional features of visual-audio content. DEVA employs an Emotional Description Generator (EDG) to transmute raw audio and visual data into textualized sentiment descriptions, thereby amplifying their emotional characteristics. These descriptions are then integrated with the source data to yield richer, enhanced features. Furthermore, DEVA incorporates the Text-guided Progressive Fusion Module (TPF), leveraging varying levels of text as a core modality guide. This module progressively fuses visual-audio minor modalities to alleviate disparities between text and visual-audio modalities. Experimental results on widely used sentiment analysis benchmark datasets, including MOSI, MOSEI, and CH-SIMS, underscore significant enhancements compared to state-of-the-art models. Moreover, fine-grained emotion experiments corroborate the robust sensitivity of DEVA to subtle emotional variations.

The challenges tied to unstructured graph data are manifold, primarily falling into node, edge, and graph-level problem categories. Graph Neural Networks (GNNs) serve as effective tools to tackle these issues. However, individual tasks often demand distinct model architectures, and training these models typically requires abundant labeled data, a luxury often unavailable in practical settings. Recently, various "prompt tuning" methodologies have emerged to empower GNNs to adapt to multi-task learning with limited labels. The crux of these methods lies in bridging the gap between pre-training tasks and downstream objectives. Nonetheless, a prevalent oversight in existing studies is the homophily-centric nature of prompt tuning frameworks, disregarding scenarios characterized by high heterogeneity. To remedy this oversight, we introduce a novel prompting strategy named HeterGP tailored for highly heterophilic scenarios. Specifically, we present a dual-view approach to capture both homophilic and heterophilic information, along with a prompt graph design that encompasses token initialization and insertion patterns. Through extensive experiments conducted in a few-shot context encompassing node and graph classification tasks, our method showcases superior performance in highly heterophilic environments compared to state-of-the-art prompt tuning techniques.

Fraud detection that aims to discern frauds from the majority of benigns has become an increasingly prominent research field. Recently, Graph Neural Networks (GNNs) have been widely applied in graph-based fraud detection due to their outstanding data analysis and mining capabilities. However, owing to the inherent homophily-heterophily mixture and class imbalance of fraud graphs, most GNNs with homophily assumption inevitably suffer from local abnormal signal loss during information propagation, posing significant challenges in situations where frauds are rare and valuable. To address the aforementioned issues, we present a novel dynamic neighborhood modeling via node-subgraph contrastive learning for graph-based fraud detection, dubbed DCL-GFD. Specifically, we first design a node abnormality estimation module from the perspective of feature, which analyses the likelihood of a node belonging to fraud or benign by comparing the feature similarity between the target node and its corresponding subgraph. We then present a dynamic neighborhood modeling mechanism guided by the abnormal probability of a node to adaptively group and aggregate neighborhood information. By this means, the target node can effectively aggregate the neighbor information from the perspective of fraud or benign, thereby preserving as much fraud characteristics that occupy minority population as possible. Extensive experiments across four real-world fraud detection datasets demonstrate the superiority and effectiveness of our proposed DCL-GFD over state-of-the-art baselines.

Graph anomaly detection aims to identify unusual patterns in graph-based data, with wide applications in fields such as web security and financial fraud detection. Existing methods typically rely on contrastive learning, assuming that a lower similarity between a node and its local subgraph indicates abnormality. However, these approaches overlook a crucial limitation: the presence of interfering edges invalidates this assumption, since it introduces disruptive noise that compromises the contrastive learning process. Consequently, this limitation impairs the ability to effectively learn meaningful representations of normal patterns, leading to suboptimal detection performance. To address this issue, we propose a Clean-View Enhanced Graph Anomaly Detection framework (CVGAD), which includes a multi-scale anomaly awareness module to identify key sources of interference in the contrastive learning process. Moreover, to mitigate bias from the one-step edge removal process, we introduce a novel progressive purification module. This module incrementally refines the graph by iteratively identifying and removing interfering edges, thereby enhancing model performance. Extensive experiments on five benchmark datasets validate the effectiveness of our approach.

As the Internet and social media evolve rapidly, distinguishing credible news from a vast amount of complex information poses a significant challenge. Due to the suddenness and instability of news events, the authenticity labels of news can potentially shift as events develop, making it crucial for fake news detection to obtain the latest event updates. Existing methods employ retrieval-augmented generation to fill knowledge gaps, but they suffer from issues such as insufficient credibility of retrieved content and interference from noisy information. We propose a dynamic knowledge update-driven model for fake news detection (DYNAMO), which leverages knowledge graphs to achieve continuous updating of new knowledge and integrates with large language models to fulfill dual functions: news authenticity detection and verification of new knowledge correctness, solving the two key problems of ensuring the authenticity of new knowledge and deeply mining news semantics. Specifically, we first construct a news-domain-specific knowledge graph. Then, we use Monte Carlo Tree Search to decompose complex news and verify them step by step. Finally, we extract and update new knowledge from verified real news texts and reasoning paths. Experimental results demonstrate that DYNAMO achieves the best performance on two real-world datasets.

Defending against adversarial attacks on graphs has become increasingly important. Graph refinement to enhance the quality and robustness of representation learning is a critical area that requires thorough investigation. We observe that representations learned from attacked graphs are often ineffective for refinement due to perturbations that cause the endpoints of perturbed edges to become more similar, complicating the defender's ability to distinguish them. To address this challenge, we propose a robust unsupervised graph learning framework that utilizes cleaner graphs to learn effective representations. Specifically, we introduce an anomaly detection model based on contrastive learning to obtain a rough graph excluding a large number of perturbed structures. Subsequently, we then propose the Graph Pollution Degree (GPD), a mutual information-based measure that leverages the encoder's representation capability on the rough graph to assess the trustworthiness of the predicted graph and refine the learned representations. Extensive experiments on four benchmark datasets demonstrate that our method outperforms nine state-of-the-art defense models, effectively defending against adversarial attacks and enhancing node classification performance.

Temporal interaction graphs (TIGs), defined by sequences of timestamped interaction events, have become ubiquitous in real-world applications due to their capability to model complex dynamic system behaviors. As a result, temporal interaction graph representation learning (TIGRL) has garnered significant attention in recent years. TIGRL aims to embed nodes in TIGs into low-dimensional representations that effectively preserve both structural and temporal information, thereby enhancing the performance of downstream tasks such as classification, prediction, and clustering within constantly evolving data environments. In this paper, we begin by introducing the foundational concepts of TIGs and emphasizing the critical role of temporal dependencies. We then propose a comprehensive taxonomy of state-of-the-art TIGRL methods, systematically categorizing them based on the types of information utilized during the learning process to address the unique challenges inherent to TIGs. To facilitate further research and practical applications, we curate the source of datasets and benchmarks, providing valuable resources for empirical investigations. Finally, we examine key open challenges and explore promising research directions in TIGRL, laying the groundwork for future advancements that have the potential to shape the evolution of this field.

Graph recommendation systems have been widely studied due to their ability to effectively capture the complex interactions between users and items. However, these systems also exhibit certain vulnerabilities when faced with attacks. The prevailing shilling attack methods typically manipulate recommendation results by injecting a large number of fake nodes and edges. However, such attack strategies face two primary challenges: low stealth and high destructiveness. To address these challenges, this paper proposes a novel graph backdoor attack method that aims to enhance the exposure of target items to the target user in a covert manner, without affecting other unrelated nodes. Specifically, we design a single-node trigger generator, which can effectively expose multiple target items to the target user by inserting only one fake user node. Additionally, we introduce constraint conditions between the target nodes and irrelevant nodes to mitigate the impact of fake nodes on the recommendation system's performance. Experimental results show that the exposure of the target items reaches no less than 50% in 99% of the target users, while the impact on the recommendation system's performance is controlled within approximately 5%.

The k-core has garnered significant attention in recent research as an effective measure of node importance within a graph. A k-core is defined as the maximal induced subgraph where each node has a degree of at least k. This paper addresses the core maximization problem: given a graph G, an integer k, and a budget b, the objective is to insert b new distinct edges into G to maximize the size of its k-core. This problem is theoretically proven to be NP-hard and APX-hard. However, the existing heuristic methods often struggle to achieve a good balance between efficiency and answer quality. In this paper, we propose a novel dynamic approach that, for the first time, uncovers the dynamic changes in node degrees. We introduce a new concept using the contribution of edges across different λ-shell components to the final solution. Based on these findings, we present the Dynamic Seed-GrowthCM method. This method selects the λ-shell component with the largest estimated benefit as the initial seed. In each iteration, depending on complete/partial growth, either a new seed is incorporated into the solution, or an existing seed undergoes growth, becoming a larger seed by adding connected components of the λ-shell component to the solution. Experimental results on ten datasets demonstrate that our algorithm significantly outperforms state-of-the-art methods in terms of solution quality on large graphs, while achieving a high computational efficiency.

Session-based Recommendation (SBR) aims to predict users’ next interaction based on their current session without relying on long-term profiles. Despite its effectiveness in privacy-preserving and real-time scenarios, SBR remains challenging due to limited behavioral signals. Prior methods often overfit co-occurrence patterns, neglecting semantic priors like item attributes. Recent studies have attempted to incorporate item attributes (e.g., category) by assigning fixed embeddings shared across all sessions. However, such approaches suffer from two key limitations: 1) Static attribute encoding fails to reflect semantic shifts under different session contexts. 2) Semantic misalignment between attribute and item ID embeddings. To address these issues, we propose attribute association driven multi-task learning for SBR, dubbed A²D-MTL. It explicitly models item categories using cross-session context to capture user potential interests and designs an adaptive sparse attention mechanism to suppress noise. Experimental results on three public datasets demonstrate the superiority of our method in recommendation accuracy (P@20) and ranking quality (MRR@20), validating the model’s effectiveness.

Graph Prompt Learning (GPL) has emerged as a promising paradigm that bridges graph pretraining models and downstream scenarios, mitigating label dependency and the misalignment between upstream pretraining and downstream tasks. Although existing GPL studies explore various prompt strategies, their effectiveness and underlying principles remain unclear. We identify two critical limitations: (1) Lack of consensus on underlying mechanisms: Despite current GPLs have advanced the field, there is no consensus on how prompts interact with pretrained models, as different strategies intervene at varying spaces within the model, i.e., input-level, layer-wise, and representation-level prompts. (2) Limited scenario adaptability: Most methods fail to generalize across diverse downstream scenarios, especially under data distribution shifts (e.g., homophilic-to-heterophilic graphs). To address these issues, we theoretically analyze existing GPL approaches and reveal that representation-level prompts essentially function as fine-tuning a simple downstream classifier, proposing that graph prompt learning should focus on unleashing the capability of pretrained models, and the classifier should adapt to downstream scenarios. Based on our findings, we propose UniPrompt, a novel GPL method that adapts any pretrained models, unleashing the capability of pretrained models while preserving the input graph. Extensive experiments demonstrate that our method can effectively integrate with various pretrained models and achieve strong performance across in-domain and cross-domain scenarios.

Graph Neural Networks (GNNs) are vulnerable to backdoor attacks. Existing defenses primarily rely on detecting structural anomalies, distributional outliers, or perturbation-induced prediction instability, which struggle to handle the more subtle, feature-based attacks that do not introduce obvious topological changes. Our empirical analysis reveals that both structure-based and feature-based attacks not only cause early loss convergence of target nodes but also induce a class-coherent loss drift, where this early convergence gradually spreads to nearby clean nodes, leading to significant distribution overlap. To address this issue, we propose LoSplit, the first training-time defense framework in graph that leverages this early-stage loss drift to accurately split target nodes. Our method dynamically selects epochs with maximal loss divergence, clusters target nodes via Gaussian Mixture Models (GMM), and applies a Decoupling-Forgetting strategy to break the association between target nodes and malicious label. Extensive experiments on multiple real- world datasets demonstrate the effectiveness of our approach, significantly reducing attack success rates while maintaining high clean accuracy across diverse backdoor attack strategies. Our code is available at: github.com/zyx924768045/LoSplit.

Graph Neural Networks (GNNs) have demonstrated strong performance across tasks such as node classification, link prediction, and graph classification, but remain vulnerable to backdoor attacks that implant imperceptible triggers during training to control predictions. While node-level attacks exploit local message passing, graph-level attacks face the harder challenge of manipulating global representations while maintaining stealth. We identify two main sources of anomaly in existing graph classification backdoor methods: structural deviation from rare subgraph triggers and semantic deviation caused by label flipping, both of which make poisoned graphs easily detectable by anomaly detection models. To address this, we propose DPSBA, a clean-label backdoor framework that learns in-distribution triggers via adversarial training guided by anomaly-aware discriminators. DPSBA effectively suppresses both structural and semantic anomalies, achieving high attack success while significantly improving stealth. Extensive experiments on real-world datasets validate that DPSBA achieves a superior balance between effectiveness and detectability compared to state-of-the-art baselines. The code is available at https://github.com/TheCoderOfs/DPSBA.

Traffic prediction plays a pivotal role in intelligent transportation systems. Most existing studies only predict traffic flow for a specific time period based on traffic data from a short period, such as an hour, overlooking the influence of periodicity present in traffic data. Moreover, most of the existing advanced methods rely on manually constructed spatio-temporal graphs for joint modeling, or use pure spatial and pure temporal modules to separately model spatial and temporal features, which limits the learning of complex spatio-temporal patterns in traffic data due to structural inadequacies in the model. To address these issues, we propose a novel approach by constructing a learnable long-range spatio-temporal graph, which can better capture complex patterns in traffic data. We introduce a new model, LLGformer, which improves upon traditional Transformer-style models, facilitating more efficient learning of traffic flow data by integrating long-range historical information. Leveraging attention mechanisms on a spatiotemporal graph enables direct interaction of information across different time slices and locations. Additionally, we propose two optimization strategies to further boost the speed of training and inference. Extensive experiments on four real-world datasets show that the new model significantly outperforms state-of-the-art methods.

Graph Contrastive Learning (GCL) is a widely adopted approach in self-supervised graph representation learning, applying contrastive objectives to produce effective representations. However, current GCL methods primarily focus on capturing implicit semantic relationships, often overlooking the structural commonsense embedded within the graph's structure and attributes, which contains underlying knowledge crucial for effective representation learning. Due to the lack of explicit information and clear guidance in general graph, identifying and integrating such structural commonsense in GCL poses a significant challenge. To address this gap, we propose a novel framework called Structural Commonsense Unveiling in Graph Contrastive Learning (Str-GCL). Str-GCL leverages first-order logic rules to represent structural commonsense and explicitly integrates them into the GCL framework. It introduces topological and attribute-based rules without altering the original graph and employs a representation alignment mechanism to guide the encoder in effectively capturing this commonsense. To the best of our knowledge, this is the first attempt to directly incorporate structural commonsense into GCL. Extensive experiments demonstrate that Str-GCL outperforms existing GCL methods, providing a new perspective on leveraging structural commonsense in graph representation learning.

Heterogeneous graph neural network (HGNN) is a popular technique for modeling and analyzing heterogeneous graphs. Most existing HGNN-based approaches are supervised or semi-supervised learning methods requiring graphs to be annotated, which is costly and time-consuming. Self-supervised contrastive learning has been proposed to address the problem of requiring annotated data by mining intrinsic properties in the given data. However, the existing contrastive learning methods are not suitable for heterogeneous graphs because they construct contrastive views only based on data perturbation or pre-defined structural properties (e.g., meta-path) in graph data while ignoring noises in node attributes and graph topologies. We develop a robust heterogeneous graph contrastive learning approach, namely HGCL, which introduces two views on respective guidances of node attributes and graph topologies and integrates and enhances them by a reciprocally contrastive mechanism to better model heterogeneous graphs. In this new approach, we adopt distinct but suitable attribute and topology fusion mechanisms in the two views, which are conducive to mining relevant information in attributes and topologies separately. We further use both attribute similarity and topological correlation to construct high-quality contrastive samples. Extensive experiments on four large real-world heterogeneous graphs demonstrate the superiority and robustness of HGCL over several state-of-the-art methods.

Recommendation systems typically rely on users’ historical behavior to infer their preferences. However, when new entries emerge, the system cannot make accurate prediction due to the lack of historical data. This is known as the “cold-start” problem, which not only limits the exposure of new items but also impacts the first experience of new users severely. Meta-learning has emerged as a promising approach to address this issue, but existing methods have limitations in dealing with the differences in user preferences and sparse monitoring data. To overcome these limitations, Dual enhanced Meta-learning with Adaptive Task Sampling is proposed. First, we propose an embedding enhancement strategy for cold nodes. Specifically, we map the cold-start embeddings into the warm space based on the common features shared across all nodes, and then add uniform noise to create the contrastive views. This strategy injects warm co-occurrence signals into the content of cold nodes, effectively enriching the feature space of cold nodes. Second, we introduce an adaptive task scheduler to measure the effectiveness of different meta-tasks and filter out the noise from invalid tasks. We assign different sampling probabilities to the tasks based on the learning process (gradient similarity) and the learning result (loss) of the meta-tasks. Finally, we consider the above two modules as auxiliary tasks for the main meta-model. Then, joint optimization is carried out through a multi-task learning framework. Experiments in three cold-start scenarios show that our approach outperforms the most advanced baselines, including traditional methods, HIN-based methods, and meta-learning-based methods.

Contrastive Learning (CL) has emerged as a popular self-supervised representation learning paradigm that has been shown in many applications to perform similarly to traditional supervised learning methods. A key component of CL is mining the latent discriminative relationships between positive and negative samples and using them as self-supervised labels. We argue that this discriminative contrastive task is, in essence, similar to a classification task, and the “either positive or negative” hard label sampling strategies are arbitrary. To solve this problem, we explore ideas from data distillation, which considers probabilistic logit vectors as soft labels to transfer model knowledge. We attempt to abandon the classical hard sampling labels in CL and instead explore self-supervised soft labels. We adopt soft sampling labels that are extracted, without supervision, from the inherent relationships in data pairs to retain more information. We propose a new self-supervised graph learning method, Distill and Contrast (D&C), for learning representations that closely approximate natural data relationships. D&C extracts node similarities from the features and structures to derive soft sampling labels, which also eliminate noise in the data to increase robustness. Extensive experimental results on real-world datasets demonstrate the effectiveness of the proposed method.

Sarcasm thrives on popular social media platforms such as Twitter and Reddit, where users frequently employ it to convey emotions in an ironic or satirical manner. The ability to detect sarcasm plays a pivotal role in comprehending individuals’ true sentiments. To achieve a comprehensive grasp of sentence semantics, it is crucial to integrate external knowledge that can aid in deciphering entities and their intricate relationships within a sentence. Although some efforts have been made in this regard, their use of external knowledge is still relatively superficial. Specifically, Knowledge-enhanced entity and relationship understanding still face significant challenges. In this paper, we propose the Knowledge Enhanced Sentiment Dependency Graph Convolutional Network (KSDGCN) framework, which constructs a commonsense-augmented sentiment graph and a commonsense-replaced dependency graph for each text to explicitly capture the role of external knowledge for sarcasm detection. Furthermore, we validate the irrational relationships between co-occurring entity pairs within sentences and background knowledge by a signed attention mechanism. We conduct experiments on four benchmark datasets, and the results show that KSDGCN outperforms existing state-of-the-art methods and is highly interpretable.

In the current Internet landscape, the rampant spread of fake news, particularly in the form of multi-modal content, poses a great social threat. While automatic multi-modal fake news detection methods have shown promising results, the lack of explainability remains a significant challenge. Existing approaches provide superficial explainability by displaying learned important components or views from well-trained networks, but they often fail to uncover the implicit deceptive patterns that reveal how fake news is fabricated. To address this limitation, we begin by predefining three typical deceptive patterns, namely image manipulation, cross-modal inconsistency, and image repurposing, which shed light on the mechanisms underlying fake news fabrication. Then, we propose a novel Neuro-Symbolic Latent Model called NSLM, that not only derives accurate judgments on the veracity of news but also uncovers the implicit deceptive patterns as explanations. Specifically, the existence of each deceptive pattern is expressed as a two-valued learnable latent variable, which is acquired through amortized variational inference and weak supervision based on symbolic logic rules. Additionally, we devise pseudo-siamese networks to capture distinct deceptive patterns effectively. Experimental results on two real-world datasets demonstrate that our NSLM achieves the best performance in fake news detection while providing insightful explanations of deceptive patterns.

Graph Neural Networks (GNNs) have received widespread attention and applications due to their excellent performance in graph representation learning. Most existing GNNs can only aggregate 1-hop neighbors in a GNN layer, so they usually stack multiple GNN layers to obtain more information from larger neighborhoods. However, many studies have shown that model performance experiences a significant degradation with the increase of GNN layers. In this paper, we first introduce the concept of distinguishability of class to indirectly evaluate the learned node representations, and verify the positive correlation between distinguishability of class and model performance. Then, we propose a Graph Neural Network guided by Distinguishability of class (Disc-GNN) to monitor the representation learning, so as to learn better node representations and improve model performance. Specifically, we first perform inter-layer filtering and initial compensation based on Local Distinguishability of Class (LDC) in each layer, so that the learned node representations have the ability to distinguish different classes. Furthermore, we add a regularization term based on Global Distinguishability of Class (GDC) to achieve global optimization of model performance. Extensive experiments on six real-world datasets have shown that the competitive performance of Disc-GNN to the state-of-the-art methods on node classification and node clustering tasks.

Graph contrastive learning (GCL) has attracted considerable attention because it can self-supervisedly extract low-dimensional representation of graph data. InfoNCE-based loss function is widely used in graph contrastive learning, which pulls the representations of positive pairs close to each other and pulls the representations of negative pairs away from each other. Recent works mainly focus on designing new augmentation methods or sampling strategies. However, we argue that the widely used InfoNCE-based methods may contain an implicit conflict which seriously confuses models when learning from negative pairs. This conflict is engendered by the encoder's message-passing mechanism and the InfoNCE loss function. As a result, the learned representations between negative samples cannot be far away from each other, compromising the model performance. To our best knowledge, this is the first time to report and analysis this conflict of GCL. To address this problem, we propose a simple but effective method called Partial ignored Graph Contrastive Learning (PiGCL). Specifically, PiGCL first dynamically captures the conflicts during training by detecting the gradient of representation similarities. It then enables the loss function to ignore the conflict, allowing the encoder to adaptively learn the ignored information without self-supervised samples. Extensive experiments demonstrate the effectiveness of our method.

Graph neural networks (GNNs) have found widespread application in modeling graph data across diverse domains. While GNNs excel in scenarios where the testing data shares the distribution of their training counterparts (in distribution, ID), they often exhibit incorrect predictions when confronted with samples from an unfamiliar distribution (out-of-distribution, OOD). To identify and reject OOD samples with GNNs, recent studies have explored graph OOD detection, often focusing on training a specific model or modifying the data on top of a well-trained GNN. Despite their effectiveness, these methods come with heavy training resources and costs, as they need to optimize the GNN-based models on training data. Moreover, their reliance on modifying the original GNNs and accessing training data further restricts their universality. To this end, this paper introduces a method to detect Graph Out-of-Distribution At Test-time (namely GOODAT), a data-centric, unsupervised, and plug-and-play solution that operates independently of training data and modifications of GNN architecture. With a lightweight graph masker, GOODAT can learn informative subgraphs from test samples, enabling the capture of distinct graph patterns between OOD and ID samples. To optimize the graph masker, we meticulously design three unsupervised objective functions based on the graph information bottleneck principle, motivating the masker to capture compact yet informative subgraphs for OOD detection. Comprehensive evaluations confirm that our GOODAT method outperforms state-of-the-art benchmarks across a variety of real-world datasets.

The Conversational Recommendation System (CRS) aims to capture user dynamic preferences and provide item recommendations based on multi-turn conversations. However, effectively modeling these dynamic preferences faces challenges due to conversational limitations, which mainly manifests as limited turns in a conversation (quantity aspect) and low compliance with queries (quality aspect). Previous studies often address these challenges in isolation, overlooking their interconnected nature. The fundamental issue underlying both problems lies in the potential abrupt changes in user preferences, to which CRS may not respond promptly. We acknowledge that user preferences are influenced by temporal factors, serving as a bridge between conversation quantity and quality. Therefore, we propose a more comprehensive CRS framework called Time-aware User-preference Tracking for Conversational Recommendation System (TUT4CRS), leveraging time dynamics to tackle both issues simultaneously. Specifically, we construct a global time interaction graph to incorporate rich external information and establish a local time-aware weight graph based on this information to adeptly select queries and effectively model user dynamic preferences. Extensive experiments on two real-world datasets validate that TUT4CRS can significantly improve recommendation performance while reducing the number of conversation turns.

Graph Contrastive Learning (GCL) has emerged as a powerful approach for generating graph representations without the need for manual annotation. Most advanced GCL methods fall into three main frameworks: node discrimination, group discrimination, and bootstrapping schemes, all of which achieve comparable performance. However, the underlying mechanisms and factors that contribute to their effectiveness are not yet fully understood. In this paper, we revisit these frameworks and reveal a common mechanism—representation scattering—that significantly enhances their performance. Our discovery highlights an essential feature of GCL and unifies these seemingly disparate methods under the concept of representation scattering. To leverage this insight, we introduce Scattering Graph Representation Learning (SGRL), a novel framework that incorporates a new representation scattering mechanism designed to enhance representation diversity through a center-away strategy. Additionally, consider the interconnected nature of graphs, we develop a topology-based constraint mechanism that integrates graph structural properties with representation scattering to prevent excessive scattering. We extensively evaluate SGRL across various downstream tasks on benchmark datasets, demonstrating its efficacy and superiority over existing GCL methods. Our findings underscore the significance of representation scattering in GCL and provide a structured framework for harnessing this mechanism to advance graph representation learning. The code of SGRL is at https://github.com/hedongxiao-tju/SGRL.

Graph Neural Networks (GNNs), known for their effective graph encoding, are extensively used across various fields. Graph self-supervised pre-training, which trains GNN encoders without manual labels to generate high-quality graph representations, has garnered widespread attention. However, due to the inherent complex characteristics in graphs, GNNs encoders pre-trained on one dataset struggle to directly adapt to others that have different node feature shapes. This typically necessitates either model rebuilding or data alignment. The former results in non-transferability as each dataset need to rebuild a new model, while the latter brings serious knowledge loss since it forces features into a uniform shape by preprocessing such as Principal Component Analysis (PCA). To address this challenge, we propose a new Feature-Universal Graph contrastive pre-training strategy (FUG) that naturally avoids the need for model rebuilding and data reshaping. Specifically, inspired by discussions in existing work on the relationship between contrastive Learning and PCA, we conducted a theoretical analysis and discovered that PCA's optimization objective is a special case of that in contrastive Learning. We designed an encoder with contrastive constraints to emulate PCA's generation of basis transformation matrix, which is utilized to losslessly adapt features in different datasets. Furthermore, we introduced a global uniformity constraint to replace negative sampling, reducing the time complexity from $O(n^2)$ to $O(n)$, and by explicitly defining positive samples, FUG avoids the substantial memory requirements of data augmentation. In cross domain experiments, FUG has a performance close to the re-trained new models. The source code is available at: https://github.com/hedongxiao-tju/FUG.

In real-world scenarios, networks (graphs) and their tasks possess unique characteristics, requiring the development of a versatile graph augmentation (GA) to meet the varied demands of network analysis. Unfortunately, most Graph Contrastive Learning (GCL) frameworks are hampered by the specificity, complexity, and incompleteness of their GA techniques. Firstly, GAs designed for specific scenarios may compromise the universality of models if mishandled. Secondly, the process of identifying and generating optimal augmentations generally involves substantial computational overhead. Thirdly, the effectiveness of the GCL, even the learnable ones, is constrained by the finite selection of GAs available. To overcome the above limitations, this paper introduces a novel unified GA module dubbed UGA after reinterpreting the mechanism of GAs in GCLs from a message-passing perspective. Theoretically, this module is capable of unifying any explicit GAs, including node, edge, attribute, and subgraph augmentations. Based on the proposed UGA, a novel generalized GCL framework dubbed Graph cOntrastive UnifieD Augmentations (GOUDA) is proposed. It seamlessly integrates widely adopted contrastive losses and an introduced independence loss to fulfill the common requirements of consistency and diversity of augmentation across diverse scenarios. Evaluations across various datasets and tasks demonstrate the generality and efficiency of the proposed GOUDA over existing state-of-the-art GCLs.

To make Graph Neural Networks (GNNs) meet the requirements of the Web, the universality and the generalization become two important research directions. On one hand, many universal GNNs are presented for semi-supervised tasks on both homophilic and non-homophilic graphs by distinguishing homophilic and heterophilic edges with the help of labels. On the other hand, self-supervised learning (SSL) algorithms on graphs are presented by leveraging the self-supervised learning schemes from computer vision and natural language processing. Unfortunately, graph universal self-supervised learning remains resolved. Most existing SSL methods on graphs, which often employ two-layer GCN as the encoder and train the mapping functions, can't alter the low-passing filtering characteristic of GCN. Therefore, to be universal, SSL must becustomized for the graph, i.e., learning the graph. However, learning the graph via universal GNNs is disabled in SSL, since their distinguishability on homophilic and heterophilic edges disappears without the labels. To overcome this difficulty, this paper proposes novel GrAph-customized Universal Self-Supervised Learning (GAUSS) by exploiting local attribute distribution. The main idea is to replace the global parameters with locally learnable propagation. To make the propagation matrix demonstrate the affinity between the nodes, the self-representative learning framework is employed with k-block diagonal regularization. Extensive experiments on synthetic and real-world datasets demonstrate its effectiveness, universality and robustness to noises.

Real-world graphs exhibit diverse structures, including homophilic and heterophilic patterns, necessitating the development of a universal Graph Contrastive Learning (GCL) framework. Nonetheless, the existing GCLs, especially those with a local focus, lack universality due to the mismatch between the input graph structure and the homophily assumption for two primary components of GCLs. Firstly, the encoder, commonly Graph Convolution Network (GCN), operates as a low-pass filter, which assumes the input graph to be homophilic. This makes it challenging to aggregate features from neighbor nodes of the same class on heterophilic graphs. Secondly, the local positive sampling regards neighbor nodes as positive samples, which is inspired by the homophily assumption. This results in feature similarity amplification for the samples from the different classes (i.e., FALSE positive samples). Therefore, it is crucial to feed the encoder and positive sampling of GCLs with homophilic graph structures. This paper presents a novel GCL framework, named gRaph cOntraStive Exploring uNiversality (ROSEN), designed to achieve this objective. Specifically, ROSEN equips a local graph structure inference module, utilizing the Block Diagonal Property (BDP) of the affinity matrix extracted from node ego networks. This module can generate the homophilic graph structure by selectively removing disassortative edges. Extensive evaluations validate the effectiveness and universality of ROSEN across node classification and node clustering tasks.

Sequential recommendation aims to capture users’ dynamic preferences. Due to the limited information in the sequence and the uncertain user behavior, data sparsity has always been a key problem. Although data augmentation methods can alleviate this issue, unreliable data can affect the performance of such models. To solve the above problems, we propose a new framework, namelyReliableData AugmentedContrastive LearningRecommender (RDCRec). Specifically, in order to generate more high-quality reliable items for data augmentation, we design a multi-attributes oriented sequence generator. It moves auxiliary information from the input layer to the attention layer for learning a better attention distribution. Then, we replace a percentage of items in the original sequence with reliable items generated by the generator as the augmented sequence, for creating a high-quality view for contrastive learning. In this way, RDCRec can extract more meaningful user patterns by using the self-supervised signals of the reliable items, thereby improving recommendation performance. Finally, we train a discriminator to identify unreplaced items in the augmented sequence thus we can update item embeddings selectively in order to increase the exposure of more reliable items and improve the accuracy of recommendation results. The discriminator, as an auxiliary model, is jointly trained with the generative task and the contrastive learning task. Large experiments on four popular datasets that are commonly used demonstrate the effectiveness of our new method for sequential recommendation.

Graph Neural Networks (GNNs), which obtain node embeddings by attribute propagates along graph topology, exhibit significant power in graph-structured data mining. However, graphs in the real world are usually text-rich, where the text can not only be represented as node attributes, but also contains valuable objective semantic structures. Moreover, the graph topology also exhibits complex subjective semantic structures, especially the heterophily where nodes from different classes are prone to build connections, making existing GNNs that work under the assumption of homophily incapable to realize generalization. To tackle aforementioned limitations, we design a new text-rich graph neural network from a unified perspective, namely SO-GNN. It can effectively enhance the expressive power of GNNs by modeling the implicit but informative subjective-objective semantics underlying the text-rich graphs. Specifically, we first introduce a new constrained Markov matrix with well-defined probabilistic diffusion dynamics to guide information propagation, where the neighbors are more appropriate and indicative in providing both local and global subjective semantics. We then construct a flexible heterogeneous text graph to gain a deeper insight into objective semantics, providing indispensable information for learning node embedding. Finally, we unite subjective and objective semantics in an end-to-end manner, so that the model can fully utilize the most relevant information for downstream tasks. Extensive experiments across various text-rich graphs with low-to-high homophily demonstrate the effectiveness and flexibility of the proposed SO-GNN over state-of-the-arts.

Heterogeneous information networks (HINs) are potent models of complex systems. In practice, many nodes in an HIN have their attributes unspecified, resulting in significant performance degradation for supervised and unsupervised representation learning. We developed an unsupervised heterogeneous graph contrastive learning approach for analyzing HINs with missing attributes (HGCA). HGCA adopts a contrastive learning strategy to unify attribute completion and representation learning in an unsupervised heterogeneous framework. To deal with a large number of missing attributes and the absence of labels in unsupervised scenarios, we proposed an augmented network to capture the semantic relations between nodes and attributes to achieve a fine-grained attribute completion. Extensive experiments on three large real-world HINs demonstrated the superiority of HGCA over several state-of-the-art methods. The results also showed that the complemented attributes by HGCA can improve the performance of existing HIN models.

Trust evaluation is critical for many applications such as cyber security, social communication, and recommender systems. Users and trust relationships among them can be seen as a graph. Graph neural networks (GNNs) show their powerful ability for analyzing graph-structural data. Very recently, existing work attempted to introduce the attributes and asymmetry of edges into GNNs for trust evaluation, while failed to capture some essential properties (e.g., the propagative and composable nature) of trust graphs. In this work, we propose a new GNN-based trust evaluation method named TrustGNN, which integrates smartly the propagative and composable nature of trust graphs into a GNN framework for better trust evaluation. Specifically, TrustGNN designs specific propagative patterns for different propagative processes of trust, and distinguishes the contribution of different propagative processes to create new trust. Thus, TrustGNN can learn comprehensive node embeddings and predict trust relationships based on these embeddings. Experiments on some widely-used real-world datasets indicate that TrustGNN significantly outperforms the state-of-the-art methods. We further perform analytical experiments to demonstrate the effectiveness of the key designs in TrustGNN.

Graph Neural Networks (GNNs) have been a prevailing technique for tackling various analysis tasks on graph data. A key premise for the remarkable performance of GNNs relies on complete and trustworthy initial graph descriptions (i.e., node features and graph structure), which is often not satisfied since real-world graphs are often incomplete due to various unavoidable factors. In particular, GNNs face greater challenges when both node features and graph structure are incomplete at the same time. The existing methods either focus on feature completion or structure completion. They usually rely on the matching relationship between features and structure, or employ joint learning of node representation and feature (or structure) completion in the hope of achieving mutual benefit. However, recent studies confirm that the mutual interference between features and structure leads to the degradation of GNN performance. When both features and structure are incomplete, the mismatch between features and structure caused by the missing randomness exacerbates the interference between the two, which may trigger incorrect completions that negatively affect node representation. To this end, in this paper we propose a general GNN framework based on teacher-student distillation to improve the performance of GNNs on incomplete graphs, namely T2-GNN. To avoid the interference between features and structure, we separately design feature-level and structure-level teacher models to provide targeted guidance for student model (base GNNs, such as GCN) through distillation. Then we design two personalized methods to obtain well-trained feature and structure teachers. To ensure that the knowledge of the teacher model is comprehensively and effectively distilled to the student model, we further propose a dual distillation mode to enable the student to acquire as much expert knowledge as possible. Extensive experiments on eight benchmark datasets demonstrate the effectiveness and robustness of the new framework on graphs with incomplete features and structure.

Graph neural network (GNN) can be formulated as the multiplication of the topology-related matrix (adjacency or Laplacian matrix) and node attribute matrix, i.e., operation in node-wise. Unfortunately, this unified formula reveals two inherent drawbacks. Firstly, the topology and node attribute are not reciprocal but biased. From employment, the topology information is repeatedly employed, while the node attribute is only used once. From parameterization perspective, the node attribute is parameterized with highly expressive MLPs, while topology is not. Secondly, the graph topology can not be fully explored. Only the local pairwise relation is explored, but the mesoscopic community structure, which is one of the most prominent characteristics of networks, is ignored. To alleviate these issues, this paper proposes the Graph Reciprocal Network (GRN) by treating node attribute and topology reciprocal. Firstly, it is illustrated that the node can be regarded and utilized as another kind of attribute. Secondly, a novel node representation scheme is proposed from the theory of Quadratic Networks, with a theoretical guarantee of the fine-grained element-wise product of the representations of the topology and attribute. Extensive experiments demonstrate the superior performance and robustness of the proposed GRN.

Graph Contrastive Learning (GCL) has recently enjoyed great success as an efficient self-supervised representation learning approach. However, the existing methods have focused on designing of contrastive modes and used data augmentation with a rigid and inefficient one-to-one sampling strategy. We adopted node neighborhoods to extend positive samplings and made avoided resorting to data augmentation to create different views. We also considered the homophily problem in Graph Neural Networks (GNNs) between the inter-class node pairs. The key novelty of our method hinged upon analyzing this GNNs problem and integrating the GCL sampling strategy with homophily discrimination, where we solved these two significant problems using one approach. We introduced a new parameterized neighbor sampling component to replace the conventional sub-optimal samplings. By keeping and updating the neighbor sets, both the positive sampling of GCL and the message passing of GNNs can be optimized. Moreover, we theoretically proved that the new method provided a lower bound of mutual information for unsupervised semantic learning, and it can also keep the lower bound with downstream tasks. In essence, our method is a new self-supervised approach, which we refer to as group discrimination, and it can make the downstream fine-tuning efficient. Our extensive empirical results demonstrate that the new method can significantly outperform the existing GCL methods because the former can solve the homophily problem in a self-supervised way with the new group discrimination method used.

Recently, the wanton dissemination of fake news on social media has adversely affected our lives, rendering automatic fake news detection a pressing issue. Current methods are often fully supervised and typically employ deep neural networks (DNN) to learn implicit relevance from labeled data, ignoring explicitly shared properties (e.g., inflammatory expressions) across fake news. To address this limitation, we propose a graph-theoretic framework, called Generalized Deep Markov Random Fields Framework (GDMRFF), that inherits the capability of deep learning while at the same time exploiting the correlations among the news articles (including labeled and unlabeled data). Specifically, we first leverage a DNN-based module to learn implicit relations, which we then reveal as the unary function of MRF. Pairwise functions with refining effects to encapsulate human insights are designed to capture the explicit association among all samples. Meanwhile, an event removal module is introduced to remove event impact on pairwise functions. Note that we train GDMRFF with the semi-supervised setting, which decreases the reliance on labeled data while maximizing the potential of unlabeled data. We further develop an Ambiguity Learning Guided MRF (ALGM) model as a concretization of GDMRFF. Experiments show that ALGM outperforms the compared methods significantly on two datasets, especially when labeled data is limited.

Most Graph Neural Networks (GNNs) follow the message-passing scheme. Residual connection is an effective strategy to tackle GNNs' over-smoothing issue and performance reduction issue on non-homophilic networks. Unfortunately, the coarse-grained residual connection still suffers from class-imbalanced over-smoothing issue, due to the fixed and linear combination of topology and attribute in node representation learning. To make the combination flexible to capture complicated relationship, this paper reveals that the residual connection needs to be node-dependent, layer-dependent, and related to both topology and attribute. To alleviate the difficulty in specifying complicated relationship, this paper presents a novel perspective on GNNs, i.e., the representations of one node in different layers can be seen as a sequence of states. From this perspective, existing residual connections are not flexible enough for sequence modeling. Therefore, a novel node-dependent residual connection, i.e., Long Short-Term Graph Memory Network (LSTGM) is proposed to employ Long Short-Term Memory (LSTM), to model the sequence of node representation. To make the graph topology fully employed, LSTGM innovatively enhances the updated memory and three gates with graph topology. A speedup version is also proposed for effective training. Experimental evaluations on real-world datasets demonstrate their effectiveness in preventing over-smoothing issue and handling networks with heterophily.

Graph Neural Networks (GNNs) have been the standard toolkit for processing non-euclidean spatial data since their powerful capability in graph representation learning. Unfortunately, their training strategy for network parameters is inefficient since it is directly inherited from classic Neural Networks (NNs), ignoring the characteristic of GNNs. To alleviate this issue, experimental analyses are performed to investigate the knowledge captured in classifier parameters during network training. We conclude that the parameter features, i.e., the column vectors of the classifier parameter matrix, are cluster representations with high discriminability. And after a theoretical analysis, we conclude that the discriminability of these features is obtained from the feature propagation from nodes to parameters. Furthermore, an experiment verifies that compared with cluster centroids, the parameter features are more potential for augmenting the feature propagation between nodes. Accordingly, a novel GNN-specific training framework is proposed by simultaneously updating node representations and classifier parameters via a unified feature propagation scheme. Moreover, two augmentation schemes are implemented for the framework, named Full Propagation Augmentation (FPA) and Simplified Full Propagation Augmentation (SFPA). Specifically, FPA augmentates the feature propagation of each node with the updated classifier parameters. SFPA only augments nodes with the classifier parameters corresponding to their clusters. Theoretically, FPA is equivalent to optimizing a novel graph learning objective, which demonstrates the universality of the proposed framework to existing GNNs. Extensive experiments demonstrate the superior performance and the universality of the proposed framework.

Due to the simplicity, intuition and explanation, most Graph Neural Networks (GNNs) are proposed by following the pipeline of message passing. Although they achieve superior performances in many tasks, propagation-based GNNs possess three essential drawbacks. Firstly, the propagation tends to produce smooth effect, which meets the inductive bias of homophily, and causes two serious issues: over-smoothing issue and performance drop on networks with heterophily. Secondly, the propagations to each node are irrelevant, which prevents GNNs from modeling high-order relation, and cause the GNNs fragile to the attributes noises. Thirdly, propagation-based GNNs may be fragile to topology noise, since they heavily relay on propagation over the topology. Therefore, the propagation, as the key component of most GNNs, may be the essence of some serious issues in GNNs. To get to the root of these issue, this paper attempts to replace the propagation with a novel local operation. Quantitative experimental analysis reveals: 1) the existence of low-rank characteristic in the node attributes from ego-networks and 2) the performance improvement by reducing its rank. Motivated by this finding, this paper propose the Low-Rank GNNs, whose key component is the low-rank attribute matrix approximation in ego-network. The graph topology is employed to construct the ego-networks instead of message propagation, which is sensitive to topology noises. The proposed Low-Rank GNNs posses some attractive characteristics, including robust to topology and attribute noises, parameter-free and parallelizable. Experimental evaluations demonstrate the superior performance, robustness to noises and universality of the proposed Low-Rank GNNs.

Network embedding which aims to learn a low dimensional representation of nodes is a powerful technique for network analysis. While network embedding for networks with complete attributes has been widely investigated, in many real-world applications the attributes of partial nodes are unobserved (i.e., missing) due to privacy concern or resource limit. Very recently, several network embedding methods have been proposed for attribute-missing networks. They first complete the missing attributes and then use the complemented network to learn network embedding. The parameters of these two processes cannot be adjusted by each other, resulting in compromised results. To address this problem, we propose a unified model in which the process of completing missing attributes and the process of learning embedding are not separated but closely intertwined. Being specific, completing missing attributes is under the guidance of learning network representation via mutual information maximization, and the complemented attributes directly enter network representation module which will generate further feedback for completing missing attributes. We further impose attribute-structure relationship constraint for completing missing attributes by designing a new generative adversarial networks (GANs) model. To the best of our knowledge, this is the first unified model for attribute-missing network embedding. Empirical results on real-world datasets show the superiority of our new method over other state-of-the-art methods on four network analysis tasks, including node classification, node clustering, link prediction, and network visualization.

Network embedding which is to learn a low dimensional representation of nodes in a network has been used in many network analysis tasks. Some network embedding methods, including those based on Generative Adversarial Networks (GAN) (a promising deep learning model), have been proposed recently. Existing GAN-based methods typically use GAN to learn a Gaussian distribution as a prior for network embedding, which makes it difficult to distinguish the node representation from Gaussian distribution. It did not apply the adversarial learning strategy on the representation mechanism but just on representation results. Thus, it does not make full use of the essential advantage of GAN, and leads to compromised performance of the method. To address this problem, we propose a novel adversarial learning framework consisting of three players for network embedding, which applies the adversarial learning strategy on the representation mechanism, called Adversarial representation mechanism GAN (ArmGAN). Specifically, the first two players, named encoder and competitor, aim to learn two different representation mechanisms (i.e., two ways projecting data onto latent space). They compete with each other to improve their representation mechanisms. The third player is the discriminator, which discriminate the representation mechanism of the encoder from that of the competitor. In addition, we design a perturbation strategy to produce fake networks from the original network, and feed the fake networks to the competitor to obtain a “fake” representation mechanism. We evaluated ArmGAN on a variety of tasks including node clustering, node classification, link prediction and visualization. Moreover, we compared ArmGAN with 10 state-of-the-art methods (including DGI, which is well-known for its high accuracy) on 7 real-world networks. The experimental results show the significant superiority of ArmGAN over the existing methods.

Anomaly detection in dynamic networks aims to find network elements (e.g., nodes, edges, subgraphs, change points) with significantly different behaviors from the vast majority, it can also devote to community detection and evolution and prediction tasks. Most existing methods focus on one specific task, that is, only detect anomalies of one type of element isolated, so they lose the ability to model the correlation and driving mechanism between different abnormal behavior. Considering that the anomaly detection of one type of element is helpful to other types of elements, i.e., the temporal evolution hidden the dynamic networks are driven by indivisible behavior patterns. So in this paper, we propose a unified Generation model to analyze the dynamic network for Exploring the Abnormal Behaviors of different Scales (GEABS). It can model the relation and catch different levels (node, community and network) of anomaly with a joint statistical network model and detect the community structure and its evolution. Specifically, we denote the parameters of node popularity, community membership to generate the dynamic network with stochastic block model (SBM), we also describe the varying of node and community by dynamic process. With a well-designed generative mechanism, it can detect the change point on network level, temporal evolution on community level and abnormal behavior on node level synchronously, besides, it also detects the community structure effectively. We also propose an effective optimization algorithm with variational inference. Experimental results show that the GEABS achieves better performance on abnormal behavior and community structure compared with baselines.

Heterogeneous information network (HIN) embedding, aiming to map the structure and semantic information in a HIN to distributed representations, has drawn considerable research attention. Graph neural networks for HIN embeddings typically adopt a hierarchical attention (including node-level and meta-path-level attentions) to capture the information from meta-path-based neighbors. However, this complicated attention structure often cannot achieve the function of selecting meta-paths due to severe overfitting. Moreover, when propagating information, these methods do not distinguish direct (one-hop) meta-paths from indirect (multi-hop) ones. But from the perspective of network science, direct relationships are often believed to be more essential, which can only be used to model direct information propagation. To address these limitations, we propose a novel neural network method viaimplicitlyutilizing attention and meta-paths, which can relieve the severe overfitting brought by the current over-parameterized attention mechanisms on HIN. We first use the multi-layer graph convolutional network (GCN) framework, which performs a discriminative aggregation at each layer, along with stacking the information propagation of direct linked meta-paths layer-by-layer, realizing the function of attentions for selecting meta-paths in an indirect way. We then give an effective relaxation and improvement via introducing a new propagation operation which can be separated from aggregation. That is, we first model the whole propagation process with well-defined probabilistic diffusion dynamics, and then introduce a random graph-based constraint which allows it to reduce noise with the increase of layers. Extensive experiments demonstrate the superiority of the new approach over state-of-the-art methods.

Community detection, a fundamental task for network analysis, aims to partition a network into multiple sub-structures to help reveal their latent functions. Community detection has been extensively studied in and broadly applied to many real-world network problems. Classical approaches to community detection typically utilize probabilistic graphical models and adopt a variety of prior knowledge to infer community structures. As the problems that network methods try to solve and the network data to be analyzed become increasingly more sophisticated, new approaches have also been proposed and developed, particularly those that utilize deep learning and convert networked data into low dimensional representation. Despite all the recent advancement, there is still a lack of insightful understanding of the theoretical and methodological underpinning of community detection, which will be critically important for future development of the area of network analysis. In this paper, we develop and present a unified architecture of network community-finding methods to characterize the state-of-the-art of the field of community detection. Specifically, we provide a comprehensive review of the existing community detection methods and introduce a new taxonomy that divides the existing methods into two categories, namely probabilistic graphical model and deep learning. We then discuss in detail the main idea behind each method in the two categories. Furthermore, to promote future development of community detection, we release several benchmark datasets from several problem domains and highlight their applications to various network analysis tasks. We conclude with discussions of the challenges of the field and suggestions of possible directions for future research.

A variety of methods have been proposed for modeling and mining dynamic complex networks, in which the topological structure varies with time. As the most popular and successful network model, the stochastic block model (SBM) has been extended and applied to community detection, link prediction, anomaly detection, and evolution analysis of dynamic networks. However, all current models based on the SBM for modeling dynamic networks are designed at the community level, assuming that nodes in each community have the same dynamic behavior, which usually results in poor performance on temporal community detection and loses the modeling of node abnormal behavior. To solve the above-mentioned problem, this article proposes a hierarchical Bayesian dynamic SBM (HB-DSBM) for modeling the node-level and community-level dynamic behavior in a dynamic network synchronously. Based on the SBM, we introduce a hierarchical Dirichlet generative mechanism to associate the global community evolution with the microscopic transition behavior of nodes near-perfectly and generate the observed links across the dynamic networks. Meanwhile, an effective variational inference algorithm is developed and we can easy to infer the communities and dynamic behaviors of the nodes. Furthermore, with the two-level evolution behaviors, it can identify nodes or communities with abnormal behavior. Experiments on simulated and real-world networks demonstrate that HB-DSBM has achieved state-of-the-art performance on community detection and evolution. In addition, abnormal evolutionary behavior and events on dynamic networks can be effectively identified by our model.

Graph Convolutional Network (GCN) has shown remarkable potential of exploring graph representation. However, the GCN aggregating mechanism fails to generalize to networks with heterophily where most nodes have neighbors from different classes, which commonly exists in real-world networks. In order to make the propagation and aggregation mechanism of GCN suitable for both homophily and heterophily (or even their mixture), we introduce block modelling into the framework of GCN so that it can realize “block-guided classified aggregation”, and automatically learn the corresponding aggregation rules for neighbors of different classes. By incorporating block modelling into the aggregation process, GCN is able to automatically aggregate information from homophilic and heterophilic neighbors discriminately according to their homophily degree. We compared our algorithm with state-of-art methods which deal with the heterophily problem. Empirical results demonstrate the superiority of our new approach over existing methods in heterophilic datasets while maintaining a competitive performance in homophilic datasets.

Graph Convolutional Networks (GCNs) have been widely applied in various fields due to their significant power on processing graph-structured data. Typical GCN and its variants work under a homophily assumption (i.e., nodes with same class are prone to connect to each other), while ignoring the heterophily which exists in many real-world networks (i.e., nodes with different classes tend to form edges). Existing methods deal with heterophily by mainly aggregating higher-order neighborhoods or combing the immediate representations, which leads to noise and irrelevant information in the result. But these methods did not change the propagation mechanism which works under homophily assumption (that is a fundamental part of GCNs). This makes it difficult to distinguish the representation of nodes from different classes. To address this problem, in this paper we design a novel propagation mechanism, which can automatically change the propagation and aggregation process according to homophily or heterophily between node pairs. To adaptively learn the propagation process, we introduce two measurements of homophily degree between node pairs, which is learned based on topological and attribute information, respectively. Then we incorporate the learnable homophily degree into the graph convolution framework, which is trained in an end-to-end schema, enabling it to go beyond the assumption of homophily. More importantly, we theoretically prove that our model can constrain the similarity of representations between nodes according to their homophily degree. Experiments on seven real-world datasets demonstrate that this new approach outperforms the state-of-the-art methods under heterophily or low homophily, and gains competitive performance under homophily.

By interpreting Graph Neural Networks (GNNs) as the message passing from the spatial perspective, their success is attributed to Laplacian smoothing. However, it also leads to serious over-smoothing issue by stacking many layers. Recently, many efforts have been paid to overcome this issue in semi-supervised learning. Unfortunately, it is more serious in unsupervised node representation learning task due to the lack of supervision information. Thus, most of the unsupervised or self-supervised GNNs often employ \textit{one-layer GCN} as the encoder. Essentially, the over-smoothing issue is caused by the over-simplification of the existing message passing, which possesses two intrinsic limits: blind message and uniform passing. In this paper, a novel Diverse and Interactive Message Passing (DIMP) is proposed for self-supervised learning by overcoming these limits. Firstly, to prevent the message from blindness and make it interactive between two connected nodes, the message is determined by both the two connected nodes instead of the attributes of one node. Secondly, to prevent the passing from uniformness and make it diverse over different attribute channels, different propagation weights are assigned to different elements in the message. To this end, a natural implementation of the message in DIMP is the element-wise product of the representations of two connected nodes. From the perspective of numerical optimization, the proposed DIMP is equivalent to performing an overlapping community detection via expectation-maximization (EM). Both the objective function of the community detection and the convergence of EM algorithm guarantee that DMIP can prevent from over-smoothing issue. Extensive evaluations on node-level and graph-level tasks demonstrate the superiority of DIMP on improving performance and overcoming over-smoothing issue.

Graph-Convolution-based methods have been successfully applied to representation learning on homophily graphs where nodes with the same label or similar attributes tend to connect with one another. Due to the homophily assumption of Graph Convolutional Networks (GCNs) that these methods use, they are not suitable for heterophily graphs where nodes with different labels or dissimilar attributes tend to be adjacent. Several methods have attempted to address this heterophily problem, but they do not change the fundamental aggregation mechanism of GCNs because they rely on summation operators to aggregate information from neighboring nodes, which is implicitly subject to the homophily assumption. Here, we introduce a novel aggregation mechanism and develop a RAndom Walk Aggregation-based Graph Neural Network (called RAW-GNN) method. The proposed approach integrates the random walk strategy with graph neural networks. The new method utilizes breadth-first random walk search to capture homophily information and depth-first search to collect heterophily information. It replaces the conventional neighborhoods with path-based neighborhoods and introduces a new path-based aggregator based on Recurrent Neural Networks. These designs make RAW-GNN suitable for both homophily and heterophily graphs. Extensive experimental results showed that the new method achieved state-of-the-art performance on a variety of homophily and heterophily graphs.

Graph Neural Networks have been widely employed for multimodal fusion and embedding. To overcome over-smoothing issue, residual connections, which are designed for alleviating vanishing gradient problem in NNs, are adopted in Graph Neural Networks (GNNs) to incorporate local node information. However, these simple residual connections are ineffective on networks with heterophily, since the roles of both convolutional operations and residual connections in GNNs are significantly different from those in classic NNs. By considering the specific smoothing characteristic of graph convolutional operation, deep layers in GNNs are expected to focus on the data which can't be properly handled in shallow layers. To this end, a novel and universal Difference Residual Connections (DRC), which feed the difference of the output and input of previous layer as the input of the next layer, is proposed. Essentially, Difference Residual Connections is equivalent to inserting layers with opposite effect (e.g., sharpening) into the network to prevent the excessive effect (e.g., over-smoothing issue) induced by too many layers with the similar role (e.g., smoothing) in GNNs. From the perspective of optimization, DRC is the gradient descent method to minimize an objective function with both smoothing and sharpening terms. The analytic solution to this objective function is determined by both graph topology and node attributes, which theoretically proves that DRC can prevent over-smoothing issue. Extensive experiments demonstrate the superiority of DRC on real networks with both homophily and heterophily, and show that DRC can automatically determine the model depth and be adaptive to both shallow and deep models with two complementary components.

To alleviate the unfavorable effect of noisy topology in Graph Neural networks (GNNs), some efforts perform the local topology refinement through the pairwise propagation weight learning and the multi-channel extension. Unfortunately, most of them suffer a common and fatal drawback: irrelevant propagation to one node and in multi-channels. These two kinds of irrelevances make propagation weights in multi-channels free to be determined by the labeled data, and thus the GNNs are exposed to overfitting. To tackle this issue, a novel Orthogonal Propagation with Ego-Network modeling (OPEN) is proposed by modeling relevances between propagations. Specifically, the relevance between propagations to one node is modeled by whole ego-network modeling, while the relevance between propagations in multi-channels is modeled via diversity requirement. By interpreting the propagations to one node from the perspective of dimension reduction, propagation weights are inferred from principal components of the ego-network, which are orthogonal to each other. Theoretical analysis and experimental evaluations reveal four attractive characteristics of OPEN as modeling high-order relationships beyond pairwise one, preventing overfitting, robustness, and high efficiency.

Although existing Graph Neural Networks (GNNs) based on message passing achieve state-of-the-art, the over-smoothing issue, node similarity distortion issue and dissatisfactory link prediction performance can’t be ignored. This paper summarizes these issues as the interference between topology and attribute for the first time. By leveraging the recently proposed optimization perspective of GNNs, this interference is analyzed and ascribed to that the learned representation in GNNs essentially compromises between the topology and node attribute. To alleviate the interference, this paper attempts to break this compromise by proposing a novel objective function, which fits node attribute and topology with different representations and introduces mutual exclusion constraints to reduce the redundancy in both representations. The mutual exclusion employs the statistical dependence, which regards the representations from topology and attribute as the observations of two random variables, and is implemented with Hilbert-Schmidt Independence Criterion. Derived from the novel objective function, a novel GNN, i.e., Graph Neural Network Beyond Compromise (GNN-BC), is proposed to iteratively updates the representations of topology and attribute by simultaneously capturing semantic information and removing the common information, and the final representation is the concatenation of them. The performance improvements on node classification and link prediction demonstrate the superiority of GNN-BC on relieving the interference.

Graph neural networks (GNNs) have gained significant success in graph representation learning and become the go-to approach for many graph-based tasks. Despite their effectiveness, the performance of GNNs is known to decline gradually as the number of layers increases. This attenuation is mainly caused by noise propagation, which refers to the useless or negative information propagated (directly or indirectly) from other nodes during the multi-layer graph convolution for node representation learning. This noise increases more severely as the layers of GNNs deepen, which is also a main reason of over-smoothing. In this paper, we propose a new convolution strategy for GNNs to address this problem via suppressing the noise propagation. Specifically, we first find that the feature propagation process of GNNs can be taken as a Markov chain. And then, based on the idea of Markov clustering, we introduce a new graph inflation layer (i.e., using a power function over the distribution) into GNNs to prevent noise propagating from local neighbourhoods to the whole graph with the increase of network layers. Our method is simple in design, which does not require any changes on the original basis and therefore can be easily extended. We conduct extensive experiments on real-world networks and have a stable improved performance as the network depth increases over existing GNNs.

Graph neural network (GNN) has become a popular tool to analyze the graph data. Existing GNNs only focus on networks with first-order dependency, that is, conventional networks following the Markov property. However, many networks in real life own the higher-order dependency, such as click-stream data where the choice of the next page depends not only on the current page but also on previous pages. This kind of sequential data from complex systems (including natural dependencies) are often ignored by existing GNNs which makes them ineffective. To address this problem, we propose for the first time new GNN approaches for higher-order networks in this paper. First, we form sequence fragments by the current node and its predecessor nodes of different orders as candidate higher-order dependencies. When the fragment significantly affects the probability distribution of different successor nodes of the current node, we include it in the higher-order dependency set. We formulize the network with higher-order dependency as an augmented conventional first-order network, and then feed it into GNNs to derive network embeddings. Moreover, we further propose a new end-to-end GNN framework for dealing with higher-order networks directly in the model. Specifically, the higher-order dependency is used as the neighbor aggregation controller when the node is embedded and updated. In the graph convolutional layer, in addition to the first-order neighbor information, we also aggregate the middle node information from the higher-order dependency segment. We finally test the new approaches on three real networks with higher-order dependency, and compare with some state-of-the-art methods. The results show significant improvements of the new approaches which consider higher-order dependency.

Directed and undirected probabilistic graphical models have been successfully used in community detection in recent years, but existing graphical model based methods usually only use one type of probabilistic graphical model to discover communities. However, directed and undirected graphical models have their own advantages for characterizing different network information (attribute information and network topology). Intuitively, we can make use of the merit of both kinds of models by combining them into a unified model. However, combining directed and undirected graphical models is difficult, as they have different properties which prevent parameter sharing and joint training. In this article, we propose a unified model which integrates directed and undirected graphical models by transforming both them into factor graph. In addition, as network topology and attribute information may contain different degrees of noises, we add a selective attention layer to learn the reliable weight of each type of information source in node granularity. For training the model, we derive an iterative belief propagation algorithm to train all the parameters simultaneously. Extensive experiments on real networks and artificial benchmarks show the superiority of our approach over existing methods.

Discovering communities is an essential step in the analysis of complex systems, and it has two purposes: to identify functional modules and to interpret semantics. However, most of the existing community detection methods only focused on identify communities, while learning the semantics interpretation of communities has not been fully studied. In this paper, we focused on the problem of identifying communities and learning the semantics interpretation of modules jointly in an end-to-end model. We designed a novel generative model which combines two closely related parts, one for community discovery and the other for content clustering and semantics interpretation. By extracting the potential correlation between these two parts, our new method is not only robust to discovering communities, but also able to provide a community with more than one semantic topic. As for model inference, we developed a variational algorithm from a Bayesian point of view. Experimental results on the artificial benchmark and real networks showed the superior performance of the proposed approach over existing methods in terms of effectiveness and efficiency. We also analyzed semantic interpretability of community detection results through a case study over a large-scale music platform dataset.

Network representation learning or embedding aims to project the network into a low-dimensional space that can be devoted to different network tasks. Temporal networks are an important type of network whose topological structure changes over time. Compared with methods on static networks, temporal network embedding (TNE) methods are facing three challenges: 1) it cannot describe the temporal dependence across network snapshots; 2) the node embedding in the latent space fails to indicate changes in the network topology; and 3) it cannot avoid a lot of redundant computation via parameter inheritance on a series of snapshots. To overcome these problems, we propose a novel TNE method named temporal network embedding method based on the VAE framework (TVAE), which is based on a variational autoencoder (VAE) to capture the evolution of temporal networks for link prediction. It not only generates low-dimensional embedding vectors for nodes but also preserves the dynamic nonlinear features of temporal networks. Through the combination of a self-attention mechanism and recurrent neural networks, TVAE can update node representations and keep the temporal dependence of vectors over time. We utilize parameter inheritance to keep the new embedding close to the previous one, rather than explicitly using regularization, and thus, it is effective for large-scale networks. We evaluate our model and several baselines on synthetic data sets and real-world networks. The experimental results demonstrate that TVAE has superior performance and lower time cost compared with the baselines.

Graph Neural Networks (GNNs) have demonstrated great power in many network analytical tasks. However, graphs (i.e., networks) in the real world are usually text-rich, implying that valuable semantic information needs to be carefully considered. Existing GNNs for text-rich networks typically treat text as attribute words alone, which inevitably leads to the loss of important semantic structures, limiting the representation capability of GNNs. In this paper, we propose an end-to-end adaptive semantic architecture of graph convolutional networks, namely AS-GCN, which unifies neural topic model and graph convolutional networks, for text-rich network representation. Specifically, we utilize a neural topic model to extract the global topic semantics, and accordingly augment the original text-rich network into a tri-typed heterogeneous network, capturing both the local word-sequence semantic structure and the global topic semantic structure from text. We then design an effective semantic-aware propagation of information by introducing a discriminative convolution mechanism. We further propose two strategies, that is, distribution sharing and joint training, to adaptively generate a proper network structure based on the learning objective to improve network representation. Extensive experiments on text-rich networks illustrate that our new architecture outperforms the state-of-the-art methods by a significant improvement. Meanwhile, this architecture can also be applied to e-commerce search scenes, and experiments on a real e-commerce problem from JD further demonstrate the superiority of the proposed architecture over the baselines.

The vast majority of community detection algorithms assume that the networks are totally observed. However, in reality many networks cannot be fully observed. On such network is edges-missing network, where some relationships (edges) between two entities are missing. Recently, several works have been proposed to solve this problem by combining link prediction and community detection in a two-stage method or in a unified framework. However, the goal of link prediction, which is to predict as many correct edges as possible, is not consistent with the requirement for predicting the important edges for discovering community structure on edges-missing networks. Thus, combining link prediction and community detection cannot work very well in terms of detecting community structure for edges-missing network. In this paper, we propose a community self-guided generative model which jointly completes the edges-missing network and identifies communities. In our new model, completing missing edges and identifying communities are not isolated but closely intertwined. Furthermore, we developed an effective model inference method that combines a nested Expectation-Maximization (EM) algorithm and Metropolis-Hastings Sampling. Extensive experiments on real-world edges-missing networks show that our model can effectively detect community structures while completing missing edges.

Zhongfen Deng, Hao Peng, Dongxiao He, Jianxin Li, Philip Yu. Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2021.

Graph Convolutional Networks (GCNs), aiming to obtain the representation of a node by aggregating its neighbors, have demonstrated great power in tackling various analytics tasks on graph (network) data. The remarkable performance of GCNs typically relies on the homophily assumption of networks, while such assumption cannot always be satisfied, since the heterophily or randomness are also widespread in real-world. This gives rise to one fundamental question: whether networks with different structural properties should adopt different propagation mechanisms? In this paper, we first conduct an experimental investigation. Surprisingly, we discover that there are actually segmentation rules for the propagation mechanism, i.e., 1-hop, 2-hop and $k$-nearest neighbor ($k$NN) neighbors are more suitable as neighborhoods of network with complete homophily, complete heterophily and randomness, respectively. However, the real-world networks are complex, and may present diverse structural properties, e.g., the network dominated by homophily may contain a small amount of randomness. So can we reasonably utilize these segmentation rules to design a universal propagation mechanism independent of the network structural assumption? To tackle this challenge, we develop a new universal GCN framework, namely U-GCN. It first introduces a multi-type convolution to extract information from 1-hop, 2-hop and $k$NN networks simultaneously, and then designs a discriminative aggregation to sufficiently fuse them aiming to given learning objectives. Extensive experiments demonstrate the superiority of U-GCN over state-of-the-arts. The code and data are available at https://github.com/jindi-tju.

Community detection has been extensively studied for various applications. Recent research has started to explore node contents to identify semantically meaningful communities. However, links in real networks typically have semantic descriptions and communities of links can better characterize community behaviors than communities of nodes. The second issue in community finding is that the most existing methods assume network topologies and descriptive contents carry the same or compatible information of node group membership, restricting them to one topic per community, which is generally violated in real networks. The third issue is that the existing methods use top ranked words or phrases to label topics when interpreting communities, which is often inadequate for comprehension. To address these issues altogether, we propose a new Bayesian probabilistic approach for modeling real networks and developing an efficient variational algorithm for model inference. Our new method explores the intrinsic correlation between communities and topics to discover link communities and extract semantically meaningful community summaries at the same time. If desired, it is able to derive more than one topical summary per community to provide rich explanations. We present experimental results to show the effectiveness of our new approach and evaluate the method by a case study.

Community detection, aiming at partitioning a network into multiple substructures, is practically importance. Graph convolutional network (GCN), a new deep-learning technique, has recently been developed for community detection. Markov Random Fields (MRF) has been combined with GCN in the MRFasGCN method to improve accuracy. However, the existing GCN community-finding methods are semi-supervised, even though community finding is essentially an unsupervised learning problem. We developed a new GCN approach for unsupervised community detection under the framework of Autoencoder. We cast MRFasGCN as an encoder and then derived node community membership in the hidden layer of the encoder. We introduced a community-centric dual decoder to reconstruct network structures and node attributes separately in an unsupervised fashion, for faithful community detection in the input space. We designed a scheme of local enhancement to accommodate nodes to have more common neighbors and similar attributes with similar community memberships. Experimental results on real networks showed that our new method outperformed the best existing methods, showing the effectiveness of the novel decoding mechanism for generating links and attributes together over the commonly used methods for reconstructing links alone.

Network embedding which is to learn a low dimensional representation of nodes in a network has been used in many network analysis tasks. Some network embedding methods, including those based on generative adversarial networks (GAN) (a promising deep learning technique), have been proposed recently. Existing GAN-based methods typically use GAN to learn a Gaussian distribution as a priori for network embedding. However, this strategy makes it difficult to distinguish the node representation from Gaussian distribution. Moreover, it does not make full use of the essential advantage of GAN (that is to adversarially learn the representation mechanism rather than the representation itself), leading to compromised performance of the method. To address this problem, we propose to use the adversarial idea on the representation mechanism, i.e. on the encoding mechanism under the framework of autoencoder. Specifically, we use the mutual information between node attributes and embedding as a reasonable alternative of this encoding mechanism (which is much easier to track). Additionally, we introduce another mapping mechanism (which is based on GAN) as a competitor into the adversarial learning system. A range of empirical results demonstrate the effectiveness of the proposed approach.

Complex networks are widely used in the research of social and biological fields. Analyzing real community structure in networks is the key to the study of complex networks. Modularity optimization is one of the most popular techniques in community detection. However, due to its greedy characteristic, it leads to a large number of incorrect partitions and more communities than in reality. Existing methods use the modularity as a Hamiltonian at the finite temperature to solve the above problem. Nevertheless, modularity is not formalized as a statistical model in the method, which makes many statistical inference methods limited and cannot be used. Moreover, the method uses the sum-product version of belief propagation (BP) and its performance is not as good as the max-sum version, since it calculates per-variable marginal probabilities rather than the joint probability . To address these issues, we propose a novel Markov Random Field (MRF) method by formalizing modularity as an energy function based on the rich structures of MRF to represent properties and constraints of this problem, and use the max-sum BP to infer model parameters. In order to analyze our method and compare it with existing methods, we conducted experiments on both real-world and synthetic networks with ground-truth of communities, showing that the new method outperforms the state-of-the-art methods.

Overlapping community detection has triggered recent research in network analysis. One of the promising techniques for finding overlapping communities is the popular stochastic models, which, unfortunately, have some common drawbacks. They do not support an important observation that highly connected nodes are more likely to reside in the overlapping regions of communities in the network. These methods are in essence not truly unsupervised, since they require a threshold on probabilistic memberships to derive overlapping structures and need the number of communities to be specified a priori . We develop a new method to address these issues for overlapping community detection. We first present a stochastic model to accommodate the relative importance and the expected degree of every node in each community. We then infer every overlapping community by ranking the nodes according to their importance. Second, we determine the number of communities under the Bayesian framework. We evaluate our method and compare it with five state-of-the-art methods. The results demonstrate the superior performance of our method. We also apply this new method to two applications, showing its superb performance on practical problems.

Finding semantic communities using network topology and contents together is a hot topic in community detection. Existing methods often use word attributes in an indiscriminate way to help finding communities. Through analysis we find that, words in networked contents often embody a hierarchical semantic structure. Some words reflect a background topic of the whole network with all communities, some imply the high-level general topic covering several topic-related communities, and some imply the high-resolution specialized topic to describe each community. Ignoring such semantic structures often leads to defects in depicting networked contents where deep semantics are not fully utilized. To solve this problem, we propose a new Bayesian probabilistic model. By distinguishing words from either a background topic or some two-level topics (i.e., general and specialized topics), this model not only better utilizes the networked contents to help finding communities, but also provides a clearer multiplex semantic community interpretation. We then give an efficient variational algorithm for model inference. The superiority of this new approach is demonstrated by comparing with ten state-of-the-art methods on nine real networks and an artificial benchmark. A case study is further provided to show its strong ability in deep semantic interpretation of communities.

Community detection is a fundamental problem in network science with various applications. The problem has attracted much attention and many approaches have been proposed. Among the existing approaches are the latest methods based on Graph Convolutional Networks (GCN) and on statistical modeling of Markov Random Fields (MRF). Here, we propose to integrate the techniques of GCN and MRF to solve the problem of semi-supervised community detection in attributed networks with semantic information. Our new method takes advantage of salient features of GNN and MRF and exploits both network topology and node semantic information in a complete end-to-end deep network architecture. Our extensive experiments demonstrate the superior performance of the new method over state-of-the-art methods and its scalability on several large benchmark problems.

Recent research on community detection focuses on learning representations of nodes using different network embedding methods, and then feeding them as normal features to clustering algorithms. However, we find that though one may have good results by direct clustering based on such network embedding features, there is ample room for improvement. More seriously, in many real networks, some statisticallysignificant nodes which play pivotal roles are often divided into incorrect communities using network embedding methods. This is because while some distance measures are used to capture the spatial relationship between nodes by embedding, the nodes after mapping to feature vectors are essentially not coupled any more, losing important structural information. To address this problem, we propose a general Markov Random Field (MRF) framework to incorporate coupling in network embedding which allows better detecting network communities. By smartly utilizing properties of MRF, the new framework not only preserves the advantages of network embedding (e.g. low complexity, high parallelizability and applicability for traditional machine learning), but also alleviates its core drawback of inadequate representations of dependencies via making up the missing coupling relationships. Experiments on real networks show that our new approach improves the accuracy of existing embedding methods (e.g. Node2Vec, DeepWalk and MNMF), and corrects most wrongly-divided statistically-significant nodes, which makes network embedding essentially suitable for real community detection applications. The new approach also outperforms other state-of-the-art conventional community detection methods.

The rapid development of social media services has facilitated the communication of opinions through online news, blogs, microblogs, instant-messages, and so on. This article concentrates on the mining of readers' social sentiments evoked by social media materials. Existing methods are only applicable to a minority of social media like news portals with emotional voting information, while ignore the emotional contagion between writers and readers. However, incorporating such factors is challenging since the learned hidden variables would be very fuzzy (because of the short and noisy text in social networks). In this paper, we try to solve this problem by introducing a high-order network structure, i.e. communities. We first propose a new generative model called Community-Enhanced Social Sentiment Mining (CESSM), which 1) considers the emotional contagion between writers and readers to capture precise social sentiment, and 2) incorporates network communities to capture coherent topics. We then derive an inference algorithm based on Gibbs sampling. Empirical results show that, CESSM achieves significantly superior performance against the state-of-the-art techniques for text sentiment classification and interestingness in social sentiment mining.

The purpose of network embedding is to learn a low-dimensional representation for each node in the network. One can then use this low-dimensional representation to solve some network analysis tasks, such as node classification and node clustering. At present, there are several network embedding learning methods based on GAN (Generative Adversarial Networks) to enhance the robustness of representations. However, these methods have two drawbacks. First, they are often too difficult to be trained stably. Second, they only learn the robust representations by matching the posterior distribution of the latent representations to the given priors. On the contrary, Capsule Networks can learn a more equivariant representation of images that is more robust to the changes in pose and spatial relationships of parts of objects in images. However, there is still no research using Capsule Network for network embedding since the social network is essentially different from images. For this problem, we propose a new approach of adversarial capsule learning (ACL) for network embedding, which is the first time to use Capsule Network in the network analysis tasks. To be specific, the new model consists of two parts, a generator and discriminator. We use Graph Convolutional Networks (GCN) as the generator to learn the embedding of nodes, and use Capsule Network as the discriminator to distinguish between the real and fake samples as accurately as possible. The experimental results demonstrate the effectiveness of the proposed new method.

Markov Random Field (MRF) has been successfully used in community detection recently. However, existing MRF methods only utilize the network topology while ignore the semantic attributes. A straightforward way to combine the two types of information is that, one can first use a topic clustering model (e.g. LDA) to derive group membership of nodes by using the semantic attributes, then take this result as a prior to define the MRF model. In this way, however, the parameters of the two models cannot be adjusted by each other, preventing it from really realizing the complementation of the advantages of the two. This paper integrates LDA into MRF to form an end-to-end learning system where their parameters can be trained jointly. However, LDA is a directed graphic model whereas MRF is undirected, making their integration a challenge. To handle this problem, we first transform LDA and MRF into a unified factor graph framework, allowing sharing the parameters of the two models. We then derive an efficient belief propagation algorithm to train their parameters simultaneously, enabling our approach to take advantage of the strength of both LDA and MRF. Empirical results show that our approach compares favorably with the state-of-the-art methods.

Network embedding (NE) maps a network into a low-dimensional space while preserving intrinsic features of the network. Variational Auto-Encoder (VAE) has been actively studied for NE. These VAE-based methods typically utilize both network topologies and node semantics and treat these two types of data in the same way. However, the information of network topology and information of node semantics are orthogonal and are often from different sources; the former quantifies coupling relationships among nodes, whereas the latter represents node specific properties. Ignoring this difference affects NE. To address this issue, we develop a network-specific VAE for NE, named as NetVAE. In the encoding phase of our new approach, compression of network structures and compression of node attributes share the same encoder in order to perform co-training to achieve transfer learning and information integration. In the decoding phase, a dual decoder is introduced to reconstruct network topologies and node attributes separately. Specifically, as a part of the dual decoder, we develop a novel method based on a Gaussian mixture model and the block model to reconstruct network structures. Extensive experiments on large real-world networks demonstrate a superior performance of the new approach over the state-of-the-art methods.

Topic models have many important applications in fields such as Natural Language Processing. Topic embedding modelling aims at introducing word and topic embeddings into topic models to describe correlations between topics. Existing topic embedding methods use documents alone, which suffer from the topical fuzziness problem brought by the introduction of embeddings of semantic fuzzy words, e.g. polysemous words or some misleading academic terms. Links often exist between documents which form document networks. The use of links may alleviate this semantic fuzziness, but they are sparse and noisy which may meanwhile mislead topics. In this paper, we utilize community structure to solve these problems. It can not only alleviate the topical fuzziness of topic embeddings since communities are often believed to be topic related, but also can overcome the drawbacks brought by the sparsity and noise of networks (because community is a high-order network information). We give a new generative topic embedding model which incorporates documents (with topics) and network (with communities) together, and uses probability transition to describe the relationship between topics and communities to make it robust when topics and communities do not match. An efficient variational inference algorithm is then proposed to learn the model. We validate the superiority of our new approach on two tasks, document classifications and visualization of topic embeddings, respectively.

Markov Random Field (MRF) is a powerful framework for developing probabilistic models of complex problems. MRF models possess rich structures to represent properties and constraints of a problem. It has been successful on many application problems, particularly those of computer vision and image processing, where data are structured, e.g., pixels are organized on grids. The problem of identifying communities in networks, which is essential for network analysis, is in principle analogous to finding objects in images. It is surprising that MRF has not yet been explored for network community detection. It is challenging to apply MRF to network analysis problems where data are organized on graphs with irregular structures. Here we present a network-specific MRF approach to community detection. The new method effectively encodes the structural properties of an irregular network in an energy function (the core of an MRF model) so that the minimization of the function gives rise to the best community structures. We analyzed the new MRF-based method on several synthetic benchmarks and real-world networks, showing its superior performance over the state-of-the-art methods for community identification.

Community detection has been extensively studied for various applications, focusing primarily on network topologies. Recent research has started to explore node contents to identify semantically meaningful communities and interpret their structures using selected words. However, links in real networks typically have semantic descriptions, e.g., comments and emails in social media, supporting the notion of communities of links. Indeed, communities of links can better describe multiple roles that nodes may play and provide a richer characterization of community behaviors than communities of nodes. The second issue in community finding is that most existing methods assume network topologies and descriptive contents to be consistent and to carry the compatible information of node group membership, which is generally violated in real networks. These methods are also restricted to interpret one community with one topic. The third problem is that the existing methods have used top ranked words or phrases to label topics when interpreting communities. However, it is often difficult to comprehend the derived topics using words or phrases, which may be irrelevant. To address these issues altogether, we propose a new unified probabilistic model that can be learned by a dual nested expectation-maximization algorithm. Our new method explores the intrinsic correlation between communities and topics to discover link communities robustly and extract adequate community summaries in sentences instead of words for topic labeling at the same time. It is able to derive more than one topical summary per community to provide rich explanations. We present experimental results to show the effectiveness of our new approach, and evaluate the quality of the results by a case study.

Network embedding, aiming at learning the low-dimensional representations of nodes in a network, is a key to many network analysis tasks. All the current network embedding methods primarily explore the network topology or node attributes, while no effort has been made to analyze the edge content for network embedding. The edge content, such as the email content between two users in an email network, is often naturally associated with edges. They carry rich information to describe the interaction between nodes, and provide valuable supervision to learn the representations of nodes. In this paper, we propose a novel edge content enhanced network embedding model, which incorporates the edge content to guide the network representation learning process. We provide the efficient updating rules to infer the parameters in the model, along with theoretical analysis on correctness and convergence guarantees. Extensive experiments, in comparison with the state-of-the-arts, show the superior performance of our proposed new approach on different network analysis tasks.

Service clustering is the foundation of service discovery, recommendation and composition. Most of the existing methods mainly use service attribute information and ignore the semantic-based invocation relationships among service users. In fact, mutual invocation relationships between services occur on operations of the corresponding services, while service attributes are the whole service description. Our main challenge may be to effectively combine these two kinds of data for service clustering. To address this issue, we propose a new probabilistic generative model which contains two closely connected parts, one characterizing operation community memberships by using operation invocation relationships, and the other characterizing service cluster memberships by utilizing service attributes. The correlations between these two parts are characterized by the relationships between operation communities and service clusters. To train this model, we provide a nested expectation-maximization algorithm. Experimental results show its superior performance over the existing methods for service clustering.

Network embedding is to learn a low-dimensional representation for a network in order to capture intrinsic features of the network. It has been applied to many applications, e.g., network community detection and user recommendation. One of the recent research topics for network embedding has been focusing on exploitation of diverse information, including network topology and semantic information on nodes of networks. However, such diverse information has not been fully utilized nor adequately integrated in the existing methods, so that the resulting network embedding is far from satisfactory. In this paper, we develop a weight-free multi-component network embedding approach by network reconstruction via a deep Autoencoder. Three key components make our new approach effective, i.e., a uniformed graph representation of network topology and semantic information, enhancement to the graph representation using local network structure (i.e., pairwise relationship on nodes) by sampling with latent space regularization, and integration of the diverse information in graph forms in a deep Autoencoder. Extensive experimental results on seven real-world networks demonstrate a superior performance of our method over nine state-of-the-art methods for embedding.

The objective of discovering network communities, an essential step in complex systems analysis, is two-fold: identification of functional modules and their semantics at the same time. However, most existing community-finding methods have focused on finding communities using network topologies, and the problem of extracting module semantics has not been well studied and node contents, which often contain semantic information of nodes and networks, have not been fully utilized. We considered the problem of identifying network communities and module semantics at the same time. We introduced a novel generative model with two closely correlated parts, one for communities and the other for semantics. We developed a co-learning strategy to jointly train the two parts of the model by combining a nested EM algorithm and belief propagation. By extracting the latent correlation between the two parts, our new method is not only robust for finding communities and semantics, but also able to provide more than one semantic explanation to a community. We evaluated the new method on artificial benchmarks and analyzed the semantic interpretability by a case study. We compared the new method with eight state-of-the-art methods on ten real-world networks, showing its superior performance over the existing methods.

In social network analysis, community detection is a basic step to understand the structure, function and semantics of networks. Some conventional community detection methods may have limited performance because they merely focus on topological structure of networks. In addition to topology, content information is another significant aspect of social networks. Some state-of-the-art methods started to combine these two aspects of information, but they often assume that topology and content share the same characteristics. However, for some examples of social networks, content may mismatch with topological structure. In order to better cope with such situations, we introduce a novel community detection method under the framework of non-negative matrix factorization (NMF). Our proposed method integrates topology and content of networks, and introduces a novel adaptive parameter for controlling the contribution of content with respect to the identified mismatch degree between the topological and content information. The case study using real social networks show that our new method can simultaneously obtain community partition and the corresponding semantic descriptions. Experiments on both artificial networks and real social networks further indicate that our method outperforms some state-of-the-art methods while exhibiting more robust behaviour when the mismatch topological and content information is observed.

Community detection is an important task in social network analysis. Existing methods typically use the topological information alone, and ignore the rich information available in the content data. Recently, some researchers have noticed that user profiles can also benefit to community detection, and hence the combination of topology and node contents has become a new hot topic. Some methods using both topology and content have been proposed. However, they often suffer from two drawbacks: 1) they cannot extract a potential deep representation of the network; 2) they cannot automatically weight different information sources with adequate balance parameters. To overcome these issues, we propose a deep integration representation (DIR) algorithm via deep joint reconstruction, which is motivated by the similarity between deep feedforward auto-encoders and spectral clustering in terms of matrix reconstruction. Thanks to spectral clustering which is one of the best community detection methods, the proposed new method is also good at community discovery task. In addition, DIR has further benefit because it not only provides a nonlinear and deep representation of the network, but also learns the most suitable balance between different components automatically. We compare the proposed new approach with nine state-of-the-art community detection methods on eight real relatively large networks. The experimental results show the definite superiority of this new approach.

Discovery of communities in networks is a fundamental data analysis task. Recently, researchers have tried to improve its performance by exploiting node contents, and further interpret the communities using the derived semantics. However, the existing methods typically assume that the communities are assortative (i.e. members of each group are mostly connected to other members of the same group), and are unable to find the generalized community structure, e.g. structures with either assortative or disassortative communities (i.e. vertices of the same group have most of their connections outside their group), or a combination. In addition, these methods often assume that the network topology and node contents share the same group memberships, and thus cannot perform well when the contents mismatch with network structure. Also, they are limited to using only one topic to interpret each community. To address these two issues, we propose a new generative probabilistic model which is learned by using a nested expectation-maximization algorithm. It describes the generalized communities (based on network) and the content clusters (based on contents) separately, and further explores and models their correlation to improve as much as possible each of the communities and clusters based on the other. By depicting and utilizing this correlation, our model is not only robust with respect to the above problems, but is also able to interpret each community using more than one topic, which provides richer explanations. We validate the robustness of this proposed new approach on an artificial benchmark, and test its interpretability using a case study analysis. We finally show its definite superiority for community detection by comparing with seven state-of-the-art algorithms on eight real networks.

Detection of overlapping communities has drawn much attention lately as they are essential properties of real complex networks. Despite its influence and popularity, the well studied and widely adopted stochastic model has not been made effective for finding overlapping communities. Here we extend the stochastic model method to detection of overlapping communities with the virtue of autonomous determination of the number of communities. Our approach hinges upon the idea of ranking node popularities within communities and using a Bayesian method to shrink communities to optimize an objective function based on the stochastic generative model. We evaluated the novel approach, showing its superior performance over five state-of-the-art methods, on large real networks and synthetic networks with ground-truths of overlapping communities.

Community detection has been one of the key research topics in the analysis of networked data, which is a powerful tool for understanding organizational structures of complex networks. One major challenge in community detection is to analyze community structures for streaming networks in real-time in which changes arrive sequentially and frequently. The existing incremental algorithms are often designed for edge-grained sequential changes, which are sensitive to the processing sequence of edges. However, there exist many real-world net-works that changes occur on node-grained, i.e., node with its connecting edges is added into network simultaneously and all edges arrive at the same time. In this paper, we propose a novel incremental community detection method based on modularity optimization for node-grained streaming networks. This method takes one vertex and its connecting edges as a processing unit, and equally treats edges involved by same node. Our algorithm is evaluated on a set of real-world networks, and is compared with several representative incremental and non-incremental algorithms. The experimental results show that our method is highly effective for discovering communities in an incremental way. In addition, our algorithm even got better results than Louvain method (the famous modularity optimization algorithm using global information) in some test networks, e.g., citation networks, which are more likely to be node-grained. This may further indicate the significance of the node-grained incremental algorithms.

Discovery of communities in networks is a fundamental data analysis problem. Most of the existing approaches have focused on discovering communities of nodes, while recent studies have shown great advantages and utilities of the knowledge of communities of links. Stochastic models provides a promising class of techniques for the identification of modular structures, but most stochastic models mainly focus on the detection of node communities rather than link communities. We propose a stochastic model, which not only describes the structure of link communities, but also considers the heterogeneous distribution of community sizes, a property which is often ignored by other models. We then learn the model parameters using a method of maximum likelihood based on an expectation-maximization algorithm. To deal with large complex real networks, we extend the method by a strategy of iterative bipartition. The extended method is not only efficient, but is also able to determine the number of communities for a given network. We test our approach on both synthetic benchmarks and real-world networks including an application to a large biological network, and also compare it with two existing methods. The results demonstrate the superior performance of our approach over the competing methods for detecting link communities.

An important problem in analyzing complex networks is discovery of modular or community structures embedded in the networks. Although being promising for identifying network communities, the popular stochastic models often do not preserve node degrees, thus reducing their representation power and applicability to real-world networks. Here we address this critical problem. Instead of using a blockmodel, we adopted a random-graph null model to faithfully capture community structures by preserving in the model the expected node degrees. The new model, learned using nonnegative matrix factorization, is more accurate and robust in representing community structures than the existing methods. Our results from extensive experiments on synthetic benchmarks and real-world networks show the superior performance of the new method over the existing methods in detecting both disjoint and overlapping communities.

Detecting communities from complex networks has triggered considerable attention in several application domains. Targeting this problem, a local search based genetic algorithm (GALS) which employs a graph-based representation (LAR) has been proposed in this work. The core of the GALS is a local search based mutation technique. Aiming to overcome the drawbacks of the existing mutation methods, a concept called marginal gene has been proposed, and then an effective and efficient mutation method, combined with a local search strategy which is based on the concept of marginal gene, has also been proposed by analyzing the modularity function. Moreover, in this paper the percolation theory on ER random graphs is employed to further clarify the effectiveness of LAR presentation; A Markov random walk based method is adopted to produce an accurate and diverse initial population; the solution space of GALS will be significantly reduced by using a graph based mechanism. The proposed GALS has been tested on both computer-generated and real-world networks, and compared with some competitive community mining algorithms. Experimental result has shown that GALS is highly effective and efficient for discovering community structure.