Pre-training graph foundation models (GFMs) on text-attributed graphs (TAGs) is important for web-scale retrieval and recommendation, where graph entities are matched with textual descriptions. Existing CLIP-style graph-text aligners typically assume one-to-one correspondence: each node is pulled close only to its paired text, and all other pairs are treated as negatives. This overlooks the many-to-many relations common in real TAGs, where a node and its local neighborhood can be semantically related to multiple texts, and vice versa. Meanwhile, TAG supervision is often imperfect: noisy or weak node-text links introduce false-positive pairs, causing contrastive learning to align mismatched semantics. These limitations reveal a fundamental trade-off: leveraging expressive many-to-many signals increases semantic coverage but may propagate errors under noise, whereas strict one-to-one training is more conservative yet still suffers when mismatched pairs remain in the training set. Therefore, we propose ADAligner, a quality-aware graph–text alignment framework that adapts between expressive many-to-many and conservative one-to-one objectives based on estimated alignment reliability. ADAligner tracks batch-level reliability online and adjusts optimization accordingly—promoting soft, subgraph-level alignment when supervision is clean while emphasizing reliable one-to-one alignment by filtering low-confidence pairs under noise. We provide theoretical analysis showing that this closed-loop adaptation is stable and convergent. Experiments on nine TAG benchmarks show that, under 30% mismatched node-text supervision, ADAligner consistently improves cross-modal retrieval by 144.70% on average, zero-/few-shot node classification by 26.13%, and link prediction by 4.70% over the strongest multimodal baseline, demonstrating strong robustness to alignment noise across both unsupervised and transfer settings. Our code is available at https://github.com/karmaisacat-13/ADAligner.

This paper presents the Local Homophily Graph Neural Network (LH-GNN), a novel framework for Graph Anomaly Detection (GAD).Anomalous activities in graphs often exhibit a complex interplay of homophily and heterophily, with our analysis revealing that anomalous nodes typically display a higher degree of heterophily compared to normal nodes.Existing GNN-based methods start to incorporate heterophily modeling but fail to address two critical challenges: (1) the efficiency challenge, as traditional spectral decomposition based methods are computationally expensive, and (2) the local homophily estimation challenge, where prior knowledge of node-wise homophily ratios is often unavailable.To address these challenges, LH-GNN introduces a lightweight polynomial graph filter that dynamically adjusts to node-specific homophily ratios, enabling efficient representation learning for both normal and anomalous nodes through adaptable heterophilic and homophilic bases.This design achieves linear time complexity, significantly improving computational efficiency.Additionally, we propose an iterative prototype learning strategy to estimate local homophily values without requiring additional labels.This strategy leverages class prototypes and uncertainty measures to assign reliable pseudolabels, effectively capturing node-wise homophily.Together, these innovations enable LH-GNN to overcome the limitations of existing methods.Extensive experiments on four benchmark datasets demonstrate that LH-GNN outperforms state-of-the-art methods in both effectiveness and efficiency, achieving 4.4% improvements in detection accuracy and 11× computational speedup 1 .

The Out-of-Distribution (OOD) problem in graph-structured data is becoming increasingly important in various areas of research and applications, including social network recommendation [36], protein function detection[9, 21], etc. Furthermore, owing to the inherent multi-label properties of nodes, multi-label OOD detection remains more challenging than in multi-class scenarios. A lack of uncertainty modeling in multilabel classification methods prevents the separation of OOD nodes from in-distribution (ID) nodes. Existing uncertainty-based OOD detection methods on graphs are not applicable for multi-label scenarios because they are designed for multi-class settings. Therefore, node-level OOD detection on multi-label graphs becomes desirable but rarely touched. In this paper, we propose a novel Evidence-Based Out-of-Distribution Detection method on multi-label graphs. The evidence for multiple labels, which indicates the amount of support to suggest that a sample should be classified into a specific class, is predicted by Multi-Label Evidential Graph Neural Networks (ML-EGNNs). The joint belief is designed for multi-label opinions fusion by a comultiplication operator. Additionally, we introduce a Kernel-based Node Positive Evidence Estimation (KNPE) method to reduce errors in quantifying positive evidence. Experimental results prove both the effectiveness and efficiency of our model for multi-label OOD detection on 7 multi-label benchmarks.

Achieving the generalization of an invariant classifier from training domains to shifted test domains while simultaneously considering model fairness is a substantial and complex challenge in machine learning. Existing methods address the problem of fairness-aware domain generalization, focusing on either covariate shift or correlation shift, but rarely consider both at the same time. In this paper, we introduce a novel approach that focuses on learning a fairness-aware domain-invariant predictor within a framework addressing both covariate and correlation shifts simultaneously, ensuring its generalization to unknown test domains inaccessible during training. In our approach, data are first disentangled into content and style factors in latent spaces. Furthermore, fairness-aware domain-invariant content representations can be learned by mitigating sensitive information and retaining as much other information as possible. Extensive empirical studies on benchmark datasets demonstrate that our approach surpasses state-of-the-art methods with respect to model accuracy as well as both group and individual fairness.

Graph contrastive learning (GCL), as a popular self-supervised learning technique, has demonstrated promising capability in learning discriminative representations for diverse downstream tasks. A large body of GCL frameworks mainly work on graphs formed under homophily effect, i.e., similar nodes tend to connect with each other. In their design, the augmentation and aggregation are usually conducted indiscriminately on edges, ignoring the existence of heterophilic edges that connect dissimilar nodes. Therefore, the efficacy of GCL could greatly deteriorate on heterophilic graphs, verified by our analysis: GCL on a mixture of homophilic and heterophilic edges will generate representations that are indistinguishable across different classes in the embedding space. To address this challenge, we propose a novel GCL framework via interventional view generation. Specifically, we generate homophilic and heterophilic views through counterfactual intervention, which targets on disentangling homophilic and heterophilic structure from the original graph, such that we can capture their corresponding information using separate filters in the contrastive learning process. Since the homophilic view and the heterophilic view present different frequency signals, they are further encoded via a low-pass and a high-pass filter respectively. Extensive experiments on multiple benchmark datasets demonstrate the effectiveness of our design. Our proposed framework achieves a remarkably improved downstream performance on graphs with high heterophily while maintaining a comparable ability in learning homophilic graphs. A comprehensive study also verifies the necessity of individual designs in our framework.

Heterogeneous graphs with multiple node and edge types are prevalent in real-world scenarios. However, most methods use meta-paths on the original graph structure to learn information in heterogeneous graphs, and these methods only consider pairwise relations and rely on meta-paths. In this paper, we use simplicial complexes to extract higher-order relations containing multiple nodes from heterogeneous graphs. We also discover power-law structures in both the heterogeneous graph and the extracted simplicial complex. Thus, we propose the Simplicial Hyperbolic Attention Network (SHAN), a graph neural network for heterogeneous graphs. SHAN extracts simplicial complexes and the original graph structure from the heterogeneous graph to represent multi-order relations between nodes. Next, SHAN uses hyperbolic multi-perspective attention to learn the importance of different neighbors and relations in hyperbolic space. Finally, SHAN integrates multi-order relations to obtain a more comprehensive node representation. We conducted extensive experiments to verify the effectiveness of SHAN and the results of node classification experiments on three publicly available heterogeneous graph datasets demonstrate that SHAN outperforms representative baseline models.

As the basic element of graph-structured data, node has been recognized as the main object of study in graph representation learning. A single node intuitively has multiple node-centered subgraphs from the whole graph (e.g., one person in a social network has multiple social circles based on his different relationships). We study this intuition under the framework of graph contrastive learning, and propose a multiple node-centered subgraphs contrastive representation learning method to learn node representation on graphs in a self-supervised way. Specifically, we carefully design a series of node-centered regional subgraphs of the central node. Then, the mutual information between different subgraphs of the same node is maximized by contrastive loss. Experiments on various real-world datasets and different downstream tasks demonstrate that our model has achieved state-of-the-art results.

For example, in machine translation tasks, to achieve bidirectional translation between two languages, the source corpus is often used as the target corpus, which involves the training of two models with opposite directions. The question of which one can adapt most quickly to a domain shift is of significant importance in many fields. Specifically, consider an original distribution p that changes due to an unknown intervention, resulting in a modified distribution p*. In aligning p with p*, several factors can affect the adaptation rate, including the causal dependencies between variables in p. In real-life scenarios, however, we have to consider the fairness of the training process, and it is particularly crucial to involve a sensitive variable (bias) present between a cause and an effect variable. To explore this scenario, we examine a simple structural causal model (SCM) with a cause-bias-effect structure, where variable A acts as a sensitive variable between cause (X) and effect (Y). The two models respectively exhibit consistent and contrary cause-effect directions in the cause-bias-effect SCM. After conducting unknown interventions on variables within the SCM, we can simulate some kinds of domain shifts for analysis. We then compare the adaptation speeds of two models across four shift scenarios. Additionally, we prove the connection between the adaptation speeds of the two models across all interventions.

Exploring the differences and important patterns of nodes from the perspective of roles has gradually developed into an interesting and important topic in network analysis. However, existing role-oriented network embedding methods focus more on identifying underlying roles for static network, which leads to complex temporal behaviors being overlooked and degraded performance facing dynamic network. The few role analytics methods for dynamic networks either cannot learn general node representations or fail to discovery role transitions of nodes. In this work, we propose a unified framework RDNE (Role-oriented Dynamic Network Embedding) to tackle such challenges, which aim to learn multiple embeddings for individual nodes based on time-varying structural behaviors. Based on regular equivalence, RDNE propagates the structural features over the graph to derive the initial role-oriented representations. Then, it applies capsule network to further model the mapping between nodes and roles, which is the first time capsule network is used for role discovery. For the varying and temporal dependence within dynamic network, we utilize the Gated Recurrent Unit to compute historical information and use historical information to influence the generation of representations at the next snapshot. Comprehensive experiments on both synthetic and real-world networks validate the superiority of the proposed RDNE.

In Community Question Answering (CQA) websites, expert finding aims at seeking suitable experts to answer questions. The key is to explore the inherent relevance based on the representations of questions and experts. Existing methods usually learn these features from single view information (e.g., question title), which would be not insufficient to fully learn their representations. In this paper, we propose a personalized expert finding method with a multi-view attentive matching mechanism. We design three modules under the multi-view paradigm, including a question encoder, an intra-view encoder, and an inter-view encoder, which aims to comprehend the comprehensive relationships between experts and questions. In the question encoder, we learn the multi-view question features from its title, body and tag views respectively. In the intra-view encoder, we design an interactive attention network to capture the view-specific relevance between the target question and the historical answered questions of experts for all different views. Furthermore, in the inter-view encoder we employ a personalized attention network to aggregate different view information to learn expert/question representations. In this way, the match of the expert and question could be fully captured from the multi-view information via the intra- and inter-view mechanisms. Experimental results on six datasets demonstrate that the proposed method could achieve better performance than existing state-of-the-art methods.

Anomalous subgraph detection has been successfully applied to event detection in social media. However, the subgraph detection problembecomes challenging when the social media network incorporates abundant attributes, which leads to a multivariate network. The multivariate characteristic makes most existing methods incapable to tackle this problem effectively and efficiently, as it involves joint feature selection and subgraph detection that has not been well addressed in the current literature, especially, in the dynamic multivariate networks in which attributes evolve over time.