Clustering is a fundamental task in graph data mining, including both node-level and graph-level clustering. While the former has been extensively explored to capture local structures and features, the latter has gained attention for its ability to capture global relationships and high-level abstractions. However, existing methods often address these two tasks in isolation, which not only wastes computational resources but also fails to fully leverage the knowledge from both levels to improve each other, hindering consistent performance improvement. To this end, we propose a novel Unified Graph Clustering Network called UGCN, which employs both local and global graph information to address node- and graph-level clustering collaboratively. In detail, we design a dual-branch projector that performs joint learning at both node and graph levels. The first branch extracts node-level features and projects them into distinct cluster layers, where the derived prototypes are used to refine graph attributes and highlight clustering-friendly substructures. In parallel, the second branch captures subgraph embeddings and aggregates them into discriminative graph-level representations. we align the two branches through joint contrastive objectives to establish a bidirectional interaction: refined prototypes guide subgraph and graph-level clustering, while graph-level pseudo-labels provide feedback to enhance node-level clustering. Extensive experimental results across seven datasets demonstrate that our method significantly outperforms existing state-of-the-art approaches.

Heterophily in graphs is a key challenge for Graph Neural Networks (GNNs). By proposing various homophily measures, recent work has provided insights into how heterophily affects node classification. However, while both graph homophily and heterophily can be further refined into diverse connection patterns, previous work has largely overlooked the role of connection pattern inconsistency. In this paper, we delve deeper into heterophily and homophily by shifting from coarse-grained heterophily ratios to a unified, fine-grained formulation based on connection patterns, and we further reveal an uneven distribution and a train–test gap of these patterns. Empirical studies indicate that this inconsistency leads to severe performance disparity. To address this issue, we propose a novel two-stage method named IVQ-GNN. In the pre-training phase, IVQ-GNN encodes diverse connection patterns into a codebook that serves as an orthogonal basis for the representation space. In the fine-tuning phase, a self-attention module linearly combines these orthogonal bases to expand the learned token space of connection patterns, thereby improving adaptation to rare and out-of-distribution (OOD) patterns. Experimental results on multiple datasets demonstrate that IVQ-GNN significantly improves model performance and validate that the proposed method effectively addresses the connection pattern inconsistency. Our code is available at https://github.com/Duyx5149/IVQ-GNN.

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.

Graph Anomaly Detection (GAD) is critical in applications such as fraud prevention, cybersecurity, and social governance. While Graph Neural Networks (GNNs) have achieved remarkable success in detecting anomalies on homogeneous graphs, they face fundamental challenges in real-world heterogeneous settings involving diverse node types and imbalanced semantic richness. In heterogeneous graphs, nodes often vary significantly in semantic richness, with anomalies potentially spanning multiple types and emerging implicitly through cross-type dependencies. We identify two core limitations of existing methods: (i) the ineffective propagation of discriminative anomaly cues from informative to sparse nodes due to semantic imbalance, and (ii) conflicting optimization objectives arising from joint detection across multiple node types. To address these issues, we propose CSA-MTHGAD, a novel framework that integrates smoothness-guided cross-type semantic alignment with dynamic multi-task learning. It selectively propagates anomaly-sensitive features across types and harmonizes task-specific gradients through adaptive projection and weighting.To facilitate research, we employ two real-world heterogeneous benchmarks in the domain of social governance. Extensive experiments demonstrate that CSA-MTHGAD achieves superior performance over state-of-the-art baselines in accuracy, robustness, and generalization for multi-type anomaly detection.

The COVID-19 pandemic not only triggered a global health crisis but also amplified public panic through the rapid spread of misinformation. Understanding public sentiment and identifying the causes of sudden sentiment spikes is therefore critical for ensuring accurate information dissemination and guiding effective policymaking. However, mining such causes from social media remains challenging. Tweets collected during sentiment spike periods are often short, noisy, and dominated by repetitive background topics, making it difficult for existing topic models to separate emerging issues from long-standing discussions. To address these challenges, we propose the Sentiment Variation-aware Emerging Topics Mining Model (SVETM), a probabilistic graphical framework that leverages user sentiment variation between adjacent time windows as a guiding signal to distinguish emerging topics from background content. We further reformulate inference as a maximum a posteriori (MAP) problem and develop an efficient variational inference algorithm for scalable learning. Extensive experiments on a large-scale COVID-19 Twitter dataset demonstrate that SVETM outperforms strong baselines in terms of topic coherence, interpretability, and its ability to uncover the underlying causes of sentiment spikes.

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.

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.

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.

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.

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.

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.