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Graph Neural Networks fundamentally rely on homophily assumptions where connected nodes are expected to share similar labels, consequently suffering severe performance degradation in heterophilic graphs due to the indiscriminate neighbor aggregation mechanism. Although recent solutions have attempted to incorporate higher-order neighborhoods or reweighting schemes, they often inadvertently amplify structural noise by introducing a larger proportion of dissimilar nodes than similar ones, while simultaneously failing to capture nuanced contextual patterns due to their inability to discern subtle local structural variations across subgraphs. To holistically address these intractable and co-existing challenges, we propose the Contextual Structure Enhanced Selective Graph Convolutional Network (CSS-GCN), a novel architecture that organically synergizes contextual structure modeling with adaptive neighbor selection. Specifically, our approach employs ego-network partitioning and group fairness constraints to effectively quantify domain-invariant structural patterns, thereby countering the contextual blindness often observed in conventional GNNs. Complementarily, we design a selective propagation mechanism unifying adaptive neighborhood distribution-based similarity computation with the gated fusion of three distinct information pathways: potential homophilic neighbors identified through attribute-topology synergy, first-hop connections, and ego-representations. This dual-component framework enables nodes to dynamically filter out irrelevant signals while preserving structural consistency across diverse homophily-heterophily landscapes. Extensive validation on 10 real-world graphs demonstrates the effectiveness and superiority of our proposed approach.
Data-driven spectrum sensing is a key technology for addressing complex challenges in Cognitive Radio Networks (CRNs). Traditional methods are typically designed for simple single-band scenarios and perform poorly in practical wideband applications. In real-world systems, a single Secondary User (SU) is often restricted by energy, time, and hardware capabilities during real-time sensing. Consequently, only local and fragmented frequency information can be obtained. This partial sensing leads to a severe lack of training data. Additionally, the lack of historical records for emerging frequency bands, combined with data incompleteness due to resource constraints, creates training bottlenecks for data-driven models and limits the reliability of sensing. To address these challenges, this paper proposes a novel framework based on Collaborative Subgraph Learning and Hyperbolic Graph Neural Networks (GNNs). This approach enables Secondary Users to perform collaborative sensing through distributed subgraph learning. By utilizing GNNs to extract features and model multi-band correlations, a new distributed GNNs architecture is designed to efficiently detect wideband spectrum occupancy, even with partial observations. Within this framework, all frequency bands in the wideband spectrum pool are treated as a unified graph, while the bands observed by each SU form a subgraph. Subsequently, the complete spectrum graph is constructed through the joint training and aggregation of these subgraphs. By integrating hyperbolic geometry into GNNs, this method better captures the hierarchical structure of spectrum patterns, providing a more accurate and efficient sensing model. Experimental results demonstrate that, compared to the second-best HCNNs model, the proposed framework improves sensing accuracy by 3.8% on average across various test environments, while reducing key resource consumption by 18.4% on average.
Graph-level classification involves analyzing the property of the whole graph, which is typically solved by using graph neural networks (GNNs). Existing efforts generally assume a balanced class distribution. However, real-world data often exhibit long-tailed distributions, i.e., tail classes have significantly fewer samples than head classes, and thus directly applying GNNs is eventually biased toward the head classes, resulting in limited generalization over the tail classes. Moreover, the predictions of existing algorithms are usually not trustworthy, and the trained classifiers remain ignorant to their predictive confidence. Towards this end, in this paper we develop a principled framework called GraphEVER for long-tailed graph-level classification. Technically, GraphEVER incorporates the beliefs of multiple experts and leverages the idea of subjective logic within the Dempster-Shafer Evidence Theory (DST). It can provide the evidence and uncertainty estimation for each expert, where the evidence is parameterized by a Dirichlet distribution to model class probability distribution, and the uncertainty is quantified via a well-defined theoretical framework. In this way, diverse experts can be integrated under DST to endow the classifier with both reliability and robustness. Moreover, we propose an evidence-based routing mechanism to dynamically assign experts, such that the tail classes can receive more attention, while the head classes can reduce redundant engaged experts, further cutting down the computational cost and improving the efficiency. Extensive experiments on seven datasets verify the superiority of our proposed framework.
Many real-world datasets, such as citation networks, social networks, and molecular structures, are naturally represented as heterogeneous graphs, where nodes belong to different types and have additional features. For example, in a citation network, nodes representing ''Paper'' or ''Author'' may include attributes like keywords or affiliations. A critical machine learning task on these graphs is node classification, which is useful for applications such as fake news detection, corporate risk assessment, and molecular property prediction. Although Heterogeneous Graph Neural Networks (HGNNs) perform well in these contexts, their predictions remain opaque. Existing post-hoc explanation methods lack support for actual node features beyond one-hot encoding of node type and often fail to generate realistic, faithful explanations. To address these gaps, we propose DiGNNExplainer, a model-level explanation approach that synthesizes heterogeneous graphs with realistic node features via discrete denoising diffusion. In particular, we generate realistic discrete features (e.g., bag-of-words features) using diffusion models within a discrete space, whereas previous approaches are limited to continuous spaces. We evaluate our approach on multiple datasets and show that DiGNNExplainer produces explanations that are realistic and faithful to the model's decision-making, outperforming state-of-the-art methods.
Explaining the decision-making process of Graph Neural Networks (GNNs) is essential for improving their transparency and reliability. However, real-world graphs are often heterogeneous and subject to structural noise, posing severe challenges to the robustness of existing explanation methods. To address these issues, we propose RoHeX, a Robust Heterogeneous GNN Explainer that enhances explanation quality under noisy conditions. RoHeX begins with a theoretical analysis revealing how different heterogeneous GNN architectures amplify structural perturbations through message passing. Building on this insight, we design a denoising variational inference framework that filters noisy structures and learns robust latent graph representations. Furthermore, we incorporate relation-aware heterogeneous semantics into the explanation generation process, formulating explanation as an optimization problem under the graph information bottleneck principle. This formulation enables RoHeX to balance fidelity and compactness, producing explanations that are both semantically meaningful and structurally stable. Comprehensive experiments on multiple real-world heterogeneous graphs demonstrate that RoHeX consistently surpasses state-of-the-art baselines in explanation fidelity, robustness to structural perturbations, and explainability.
Graph Masked Autoencoders (GMAEs) have emerged as a notable self-supervised learning approach for graph-structured data. Existing GMAE models primarily focus on reconstructing node-level information, categorizing them as single-scale GMAEs. This methodology, while effective in certain contexts, tends to overlook the complex hierarchical structures inherent in many real-world graphs. For instance, molecular graphs exhibit a clear hierarchical organization in the form of the atoms-functional groups-molecules structure. Therefore, the inability of single-scale GMAE models to incorporate these hierarchical relationships often results in an inadequate capture of crucial high-level graph information, leading to a noticeable decline in performance. To address this limitation, we propose Hierarchical Graph Masked AutoEncoders (Hi-GMAE), a novel multi-scale GMAE framework designed to handle the hierarchical structures within graphs. First, Hi-GMAE constructs a multi-scale graph hierarchy through graph pooling, enabling the exploration of graph structures across different granularity levels. To ensure masking uniformity of subgraphs across these scales, we propose a novel coarse-to-fine strategy that initiates masking at the coarsest scale and progressively back-projects the mask to finer scales. Furthermore, we integrate a gradual recovery strategy with the masking process to mitigate the learning challenges posed by completely masked subgraphs. Diverging from the standard graph neural network (GNN) used in GMAE models, Hi-GMAE modifies its encoder and decoder into hierarchical structures. This entails using GNN at the finer scales for detailed local graph analysis and employing a graph transformer at coarser scales to capture global information. Such a design enables Hi-GMAE to effectively capture the multi-level information inherent in complex graph structures. Our experiments on 17 graph datasets, covering two graph learning tasks, consistently demonstrate that Hi-GMAE outperforms 29 state-of-the-art self-supervised competitors in capturing comprehensive graph information.
Graph Neural Networks (GNNs) are vulnerable to graph backdoor attacks, which poses severe risks to their deployment in safety-critical applications. Existing defenses predominantly focus on specific backdoor triggers, making them brittle and unable to generalize across different backdoor triggers with varying properties. Motivated by this limitation, this work proposes an attack-agnostic graph backdoor defense mechanism A2GBD, which does not require prior knowledge of the specific attack strategies (e.g., edge perturbation, node attribute manipulation) to achieve effective defense. A2GBD consists of suspicious node selection and defense strategy generation. The selection module selects high-suspicion nodes to enhance defense awareness, while the defense agent adaptively determines and executes defense strategies. Extensive experiments on multiple benchmark datasets demonstrate that A2GBD consistently lowers attack success rates while maintaining high clean accuracy, showing strong robustness and generalizability against diverse graph backdoor attack strategies.
Text-attributed graphs (TAGs) enhance graph learning by integrating rich textual semantics and topological context for each node. While boosting expressiveness, they also expose new vulnerabilities in graph learning through text-based adversarial surfaces. Recent advances leverage diverse backbones, such as graph neural networks (GNNs) and pre-trained language models (PLMs), to capture both structural and textual information in TAGs. This diversity raises a key question: How can we design universal adversarial attacks that generalize across architectures to assess the security of TAG models? The challenge arises from the stark contrast in how different backbones—GNNs and PLMs—perceive and encode graph patterns, coupled with the fact that many PLMs are only accessible via APIs, limiting attacks to black-box settings. To address this, we propose BadGraph, a novel attack framework that deeply elicits large language models' (LLMs) understanding of general graph knowledge to jointly perturb both node topology and textual semantics. Specifically, we design a target influencer retrieval module that leverages graph priors to construct cross-modally aligned attack shortcuts, thereby enabling efficient LLM-based perturbation reasoning. Experiments show that BadGraph achieves universal and effective attacks across GNN- and LLM-based reasoners, with up to a 76.3% performance drop, while theoretical and empirical analyses confirm its stealthy yet interpretable nature.
Identification of Influential Node Group in Attributed Graph through Explaining Graph Neural Network
Identification of influential groups of nodes in attributed graphs has applications in a wide range of real-world problems, for instance, collecting important proceedings in citation networks, or identifying essential genes for diagnosing disease in Protein-Protein Interaction networks. Previous approaches for influence maximization manipulated on the graph structure, despite their proliferation, neglect the node attribute information containing additional knowledge. In this work, we introduce Global Graph UNderstanding (GGUN), a perturbation-based framework leveraging the explanatory power of Graph Neural Networks. It takes into account the entire graph structure and node attributes simultaneously and fuses knowledge through GNN layers. Following the perturbation-based explanation, GGUN fills the gap between Deep Neural Network gradient-based feature importance analysis and discrete structure in the graph, which is formulated as a combinatorial optimization problem. Moreover, GGUN obtains an efficient solution by relaxing the infeasible combinatorial optimization problem with performance guaranteed. Evaluations of synthetic and real-world datasets show that GGUN outperforms baselines on both quantitative metrics and human-intelligible analysis.
Out-of-Distribution (OOD) detection, which seeks to identify samples deviating from the In-Distribution (ID) training distribution at test time, is crucial for building robust machine learning systems. While extensive efforts have been made for Euclidean data, OOD detection on graph-structured data remains relatively underexplored. On the one hand, the specific properties of a graph may be attributed to its substructures. On the other hand, acquiring labeled data for graph learning is typically time-consuming and labor-intensive. Toward this end, in this paper, we propose a novel kernel-enhanced graph substructure learning framework termed KEGOD for unsupervised graph OOD detection. Specifically, we introduce a learnable graph generator to construct the augmented graph view that preserves distinguishable structure information. Then, for both the input graph and augmented view, a graph neural network (GNN) branch and a graph kernel (GK) branch are incorporated to explore graph latent patterns. By performing multi-branch concordance learning on the extracted graph patterns, our KEGOD captures complementary ID structural semantics in both implicit and explicit manners, enabling reliable detection of OOD graphs through semantic inconsistency. Finally, we build a self-adaptive training mechanism to automatically control diverse sensitivities of the graph patterns. Experimental results on several public graph datasets reveal the superiority of our KEGOD. Our code is available at~ https://github.com/jamesyifan/KEGOD.
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.
Hypergraphs are the natural description of higher-order interactions among objects, widely applied in social network analysis, cross-modal retrieval, etc. Hypergraph Neural Networks (HGNNs) have become the dominant solution for learning on hypergraphs. Traditional HGNNs are extended from message passing graph neural networks, following the homophily assumption, and thus struggle with the prevalent heterophilic hypergraphs that call for long-range dependence modeling. Existing solutions enlarge the message flow through the hypergraph bottleneck, mitigating the oversquashing issue and capturing long-range dependence. However, they often accelerate the loss of representation distinguishability in the repeated aggregations, leading to oversmoothing. This dilemma motivates an interesting question: Can we develop a unified mechanism that is agnostic to both homophilic and heterophilic hypergraphs? In this paper, we achieve the best of both worlds through the lens of Riemannian geometry, which provides the potential to adjust the message passing behavior in different regions. The key insight lies in the connection between oversquashing and hypergraph bottleneck within the framework of Riemannian manifold heat flow. Building on this, we propose the novel idea of locally adapting the bottlenecks of different subhypergraphs. The core innovation of the proposed mechanism is the design of an adaptive local (heat) exchanger. Specifically, it captures the rich long-range dependencies via the Robin condition, and preserves the representation distinguishability via source terms, thereby enabling heterophily-agnostic message passing with theoretical guarantees. Based on this theoretical foundation, we present a novel Heat-Exchanger with Adaptive Locality for Hypergraph Neural Network (HealHGNN), designed as a node-hyperedge bidirectional systems with linear complexity in the number of nodes and hyperedges. Extensive experiments on both homophilic and heterophilic cases show that HealHGNN achieves the state-of-the-art performance.
Graph Neural Networks (GNNs) have become the dominant paradigm for machine learning on relational data, yet they remain susceptible to overfitting and noise in graph structures. While data augmentation has proven effective for regularization across domains, existing graph methods operate at coarse levels, such as perturbing entire structures or mixing node features. These approaches are context-agnostic and do not target the core computational process of GNNs: message passing. We introduce MessageShift, a novel fine-grained data augmentation paradigm that operates directly on the messages, the atomic units of information, as they flow through the GNN. The core idea is to apply a contextual perturbation to each message by shifting it towards or away from the center of its local neighborhood. This provides a rich regularization effect, capable of both smoothing noisy messages and sharpening distinctive ones. Extensive experiments on a wide range of benchmark datasets demonstrate that MessageShift consistently outperforms strong baselines across multiple GNN backbones.
Multi-Graph Learning (MGL) is a fundamental machine learning paradigm that represents objects as bags-of-graphs, each encoding a distinct structural property, and has broad applications in bioinformatics, chemistry, computing power networks, and software defect detection. However, the inherent scarcity of labeled data poses a significant bottleneck for supervised MGL approaches. While self-supervised contrastive learning offers a compelling solution, its direct application to MGL faces three key challenges: (1) existing graph neural networks, primarily for single-graph modeling, struggle to yield discriminative bag-level representations from bags-of-graphs; (2) conventional contrastive objectives are limited to single-level settings, failing to capture cross-hierarchical dependencies; and (3) standard data augmentation often disrupts intrinsic graph and bag structures, undermining semantic consistency. To address these issues, we propose the Hierarchical Graph-Bag-Network (HGBN), a self-supervised MGL framework that constructs hierarchical representations in the form of a graph-bag-network. HGBN employs an asymmetric hierarchical graph neural network to learn discriminative graph-level and bag-level representations, introduces cross-hierarchical contrastive objectives to align graph-level and bag-level semantics, and leverages the asymmetric network outputs to form positive and negative pairs, preserving intrinsic structural and semantic consistency. Experiments on eight benchmark multi-graph datasets demonstrate that HGBN consistently outperforms both supervised and self-supervised state-of-the-art baselines, achieving average improvements of 4.82% in accuracy and F1 score.
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.
Graph Neural Networks (GNNs) have demonstrated impressive performance across diverse graph-based tasks by leveraging message passing to capture complex node relationships. However, on large-scale real-world graphs, GNNs face two major challenges: (1) GNNs struggle to ensure scalability and efficiency as repeated aggregation of large neighborhoods incurs significant computational overhead; (2) GNNs suffer from over-smoothing, where excessive propagation makes node representations indistinguishable, hindering model expressiveness. To tackle these, we propose ScaleGNN, which adaptively fuses multi-hop node features for scalable and effective graph learning. We first compute per-hop pure-neighbor matrices to isolate exclusive structural signals, then apply lightweight fusion to balance low- and high-order information, preserving both local detail and global correlations. To curb redundancy and over-smoothing, we introduce Local Contribution Score (LCS)–based masking to prune low-relevance high-order neighbors, and impose learnable sparsity to selectively integrate valuable multi-hop features. Extensive experiments on real-world datasets show that ScaleGNN consistently outperforms state-of-the-art GNNs in both predictive accuracy and computational efficiency. The source code is available at https://github.com/lx970414/ScaleGNN.
The out-of-distribution (OOD) generalization of graph neural networks poses significant challenges in Web applications, where data resides in complex heterogeneous information networks. Such networks exhibit not only structural heterogeneity but also distribution shifts arising from evolving user behaviors and data collection biases. Conventional GNNs often struggle to identify stable patterns in such heterogeneous graphs, particularly in the absence of explicit environment labels. The core issue is that latent environments can create spurious correlations between node features, local topology, and labels. Models may then rely on these environment-specific shortcuts for predictions, failing to learn the invariant mechanisms that generalize under distribution shifts. To address these limitations, we propose InvHG (Invariant Learning on Heterogeneous Graphs via Subgraph Environment Inference), a causality-inspired framework that infers latent environments at the subgraph level, disentangles type-specific confounding effects, and leverages regularized expert fusion to learn invariant representations. Extensive experiments on heterogeneous graph OOD benchmarks demonstrate that InvHG consistently outperforms state-of-the-art methods, offering a robust solution for complex Web graph learning. The source code is available at https://github.com/mok630/InvHG.
Many real-world systems can be modeled as graphs, where nodes store and consume entities, actively produce them, or have them emerge naturally, and edges transport them between nodes. This paper studies the networked control problem on such large-scale systems, aiming to decide production and transportation over time to maximize long-term profits, subject to node or edge capacity constraints. Existing SOTAs either fail to guarantee feasibility or cannot scale to large-scale systems. We propose a two-stage policy that integrates a constrained optimization layer after a neural network to explicitly enforce constraints and ensure feasibility. By leveraging the problem structure to obtain expert actions and designing a decision-focused and differentiable loss to enable imitation learning, our method significantly improves efficiency and scalability. In small-scale systems with action dimensions in the order of 10, our method achieves 60x sample efficiency over SOTAs on average. In large-scale systems with action dimensions ranging from 100 to 100000, where SOTAs fail to train, our method converges quickly and outperforms non-learning-based baselines significantly.
In the absence of node attributes, Graph Neural Networks (GNNs) often fail to distinguish locally isomorphic nodes, leading to suboptimal performance. To compensate for this, Positional Information (PI) augmentation has emerged as a powerful technique, which generates attributes by selecting representative nodes as anchors and encoding node-to-anchor distances to other nodes. However, the performance of PI-based methods hinges on two graph-dependent choices: 1) the structural measures used for anchor selection and distance metrics, and 2) the anchor-count K. To obviate manual selections, we propose SCOUT, a model-agnostic augmentation framework that learns a graph-level selector to identify the optimal structural measure and adaptively determines the anchor-count K tailored to each graph and task. Subsequently, leveraging the heavy-tailed distribution typically observed in node centrality, SCOUT utilizes an elbow detection method on the ranked centrality curve to adaptively determine the K most representative nodes as anchors. SCOUT is model-agnostic and enhances various GNNs across downstream tasks. It achieves an improvement of 26.88% in Hits@20 for link prediction on ogbl-ddi and 4.52% accuracy points for node classification on ogbn-arxiv without original attributes; with original attributes, it also brings additional gains of 6.15% AUC on Cora and 11.69% accuracy points on ogbn-arxiv. The source code of SCOUT is available at https://github.com/seinkim01/SCOUT.git.