This paper investigates a novel and critical problem of Graph Cross-Domain Continual Fine-Tuning, which aims to adapt a large pre-trained Graph Foundation Model across diverse domains. Existing continual graph learning methods are mostly limited to incremental settings within only a single domain, and are typically trained from scratch. As a result, they fail to handle cross-domain shifts effectively, suffer from severe forgetting, and lack transferability. To address these challenges, we present G-CORMoL, Graph Continual Fine-tuning with Orthogonal, Router-driven Mixture of LoRA experts. G-CORMoL achieves effective adaptation while preserving prior knowledge by enforcing mathematical orthogonality between expert LoRA adapters, thereby eliminating interference across tasks. It further supports cross-domain knowledge transfer through a symmetric dual-driven routing mechanism that learns a global composition policy over all learned LoRA experts. In addition, it promotes expert specialization via a contrastive objective with theoretical guarantees. Extensive experiments on different cross-domain task orders demonstrate that G-CORMoL achieves robust state-of-the-art performance, not only preventing catastrophic forgetting but also leveraging accumulated knowledge to enable positive transfer.
论文检索
输入标题、作者或关键词，从 954 篇学术成果中精准定位
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 clustering aims to partition nodes into distinct clusters based on their similarity, thereby revealing relationships among nodes. Nevertheless, most existing methods do not fully utilize these edge weights. Leveraging edge weights in graph clustering tasks faces two critical challenges. (1) The introduction of edge weights may significantly increase storage space and training time, making it essential to reduce the graph scale while preserving nodes that are beneficial for the clustering task. (2) Edge weight information may inherently contain noise that negatively impacts clustering results. However, few studies can jointly optimize clustering and edge weights, which is crucial for mitigating the negative impact of noisy edges on clustering task. To address these challenges, we propose a contractile edge-weight-aware graph clustering network. Specifically, a cluster-oriented graph contraction module is designed to reduce the graph scale while preserving important nodes. An edge-weight-aware attention network is designed to identify and weaken noisy connections. In this way, we can more easily identify and mitigate the impact of noisy edges during the clustering process, thus enhancing clustering effectiveness. We conducted extensive experiments on three real-world weighted graph datasets. In particular, our model outperforms the best baseline, demonstrating its superior performance. Furthermore, experiments also show that the proposed graph contraction module can significantly reduce training time and storage space.
Social event detection (SED) enhances public awareness by clustering large-scale social messages and has been widely applied across diverse domains. While many existing frameworks adopt Graph Convolution Networks (GCN) as backbones and extend them with auxiliary modules to model complex message relationships, they rarely explore the deeper potential of GCN itself. Specifically, the core operation of GCN—smoothed feature aggregation—implicitly assumes that social information diffusion follows a single-pass and independent heat propagation process. However, real-world diffusion is inherently multi-wave and oscillatory, where different messages interact through reinforcement and interference, resembling the principle of wave–particle duality. To address this gap, we propose Physics-Inspired Graph Convolution Networks (PIGCN ), a novel model that unifies wave-based propagation and particle-like interactions. Specifically, PIGCN integrates a Helmholtz Graph Filter module to capture spectral wave propagation with oscillatory dynamics, and a Physical Interaction Force mechanism to adaptively adjust edge weights by attracting messages within the same event and repelling those across events. Building on PIGCN, we further incorporate a contextualized text encoder and timestamp encoding to form a comprehensive SED framework. Extensive experiments demonstrate that PIGCN effectively outperforms conventional GCN baselines and achieves superior performance across multiple SED datasets, establishing a new benchmark for balancing architectural simplicity with effectiveness. Our code can be found on GitHub . https://github.com/yuyongsheng1990/PIGCN
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.
Semi-supervised graph anomaly detection confronts the fundamental challenge of identifying anomalous nodes that exhibit deviations from normal graph patterns in node attributes or structure connectivity, using a small set of labeled normal nodes. Existing methods fall short in modeling the spectrum of anomaly severity, as they generate anomalies in a single, undifferentiated step. This oversight restricts the detection of complex anomalies. In this paper, we propose a progressive anomaly generation network (PAGE) to overcome this limitation. PAGE enhances complex anomaly detection through progressive attribute-structure perturbation: It first injects attribute noise into selected normal nodes to generate pseudo-anomalous nodes. Then it applies progressive structure perturbation to create hybrid anomalies that simulate the evolution from mild to complex perturbations. Importantly, the proposed anomaly progressive constraint loss enforces that hybrid anomalies exhibit a higher degree of abnormality than primary anomalies, enhancing the model's anomaly quantification capability. PAGE further integrates deviation constraint loss and classification loss to optimize node representations and improve prediction. Extensive experiments on benchmark datasets demonstrate that PAGE significantly outperforms state-of-the-art methods, demonstrating its effectiveness for graph anomaly detection.
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.
Federated graph-level clustering (FGC) provides an effective solution for analyzing decentralized graph data with privacy protection. Existing methods typically assume that all clients have the same number of clusters. This assumption simplifies the learning task and has achieved preliminary success. However, this assumption rarely holds in practice, as clients often exhibit substantial heterogeneity in both data distributions and semantic granularity. As a result, cluster-specific knowledge becomes misaligned during server-side aggregation, which ultimately degrades the overall clustering performance. To address this challenge, we propose a novel Federated Graph Clustering under Inter-Client Cluster Number Discrepancy (FedCND) framework, which aligns inter-client heterogeneous distributions by decoupling graph data into public and private patterns. Specifically, after initial local training and clustering on each client, we design a public learner and a private learner to model public and private graph data, respectively. Only anonymized, cluster-level public information is uploaded to the server, while private information remains local. On the server, cluster-level public prototypes are aggregated based on affinities between reconstructed cluster-level graphs, enabling privacy-preserving prototype alignment across clients with heterogeneous cluster numbers and mitigating interference from misaligned information during global aggregation. Finally, private subgraphs derive client-specific prototypes through local relearning, which are subsequently fused with globally oriented public prototypes for better clustering. Extensive experiments demonstrate that the proposed FedCND achieves an average of 4.9% accuracy improvement against current state-of-the-art methods.
For complex networks such as the Web, the Network Dismantling (ND) problem, which asks for the minimum-cost removal of nodes that destroys the giant connected component in the network, is significant in system robustness and misinformation containment. In this paper, we propose a heuristic algorithm, IG+, which is based on reverse dismantling and incorporates novel optimizations. Besides, we design two dynamic algorithms, CCRT-ins and CCRT-rem, employing tree-like indexes to update dismantling results efficiently. Experiments show that our methods outperform state-of-the-art approaches in both effectiveness and efficiency, and can dismantle 10-million-scale networks at arbitrary granularity in a few minutes.
Graph-level anomaly detection (GLAD) is a critical task to identify graphs with abnormal properties in various domains, ranging from fraudulent social networks to malicious botnets on online platforms. The dominant paradigm for existing GLAD detectors has been partially unsupervised, relying on training data composed exclusively of normal samples. However, this partially unsupervised paradigm inevitably requires a costly expert filtering process to ensure the training data is free of anomalies. This creates a significant gap between current approaches and the real-world necessity of a fully unsupervised paradigm, which involves training a model directly on real-world data ''as-is'', with its inherent mix of normal and anomalous samples. To bridge this gap, we incorporate uncertainty learning into GLAD to promote fully unsupervised learning. We propose two frameworks: Score Uncertainty Learning (SUL) and Graph-data Uncertainty Learning (GUL). Specifically, SUL enhances existing GLAD detectors by modeling uncertainty through Gaussian distributions over the detectors' predictions, adaptively attenuating the influence of potential anomalies. GUL is an end-to-end framework that iteratively optimizes anomaly detection and uncertainty modeling via an Expectation-Maximization algorithm. In addition, we develop a dedicated loss that utilizes potential anomalies to enhance the effectiveness and robustness of GUL. Empirical results on sixteen benchmark datasets, covering real-world graphs from social networks and online platforms, demonstrate the superiority of our methods and highlight the promise of incorporating uncertainty into fully unsupervised GLAD.
Every graph hides a tree: through tree decomposition—a foundational tool in modern graph theory with broad applications such as in computational power networks, any network can be unfolded into a hierarchy of overlapping vertex bags whose backbone is a tree. Leveraging this powerful lens, we propose Topological Decomposition for Self-supervised Learning (TopDSL), a framework that injects multi-scale signals into graph representation learning. Concretely, we: 1) decompose the input graph into tree structures with bags representing local structural contexts; 2) compute bag-level roles via closeness centrality for nodes and local edge betweenness for edges, and aggregate these scores across bags to capture context-dependent importance (e.g., local structural bridges); 3) convert the resulting importance and attribute-stability scores into a context-aware augmentation policy that adaptively perturbs nodes, edges, and features—preserving local bridges, honoring multi-community vertices, and attenuating noisy global hubs; 4) construct a new structural similarity loss for contrastive learning, which fuses traditional graph-based proximity with a novel tree-based similarity derived from node co-occurrence in decomposition bags; 5) demonstrate that our framework achieves superior performance over state-of-the-art baselines on various graph learning benchmarks.
Timely detection of anomalous interactions between different entities is crucial for the security and stability of Web-related systems, which are often modeled as streaming graphs. A major issue in existing anomaly detection methods is the effectiveness and the corresponding interpretability. In this paper, we introduce a novel evaluation metric, namely Interaction Willingness, to measure the propensity for entity interactions. Based on this metric, we design two efficient anomaly detection algorithms, AnoD and AnoC, tailored for real-time detection of two prevalent types of anomalous interaction behaviors (dense-type and counter-type), respectively. Notably, we adopt and extend the High-Order Count-Min Sketch for the implicit storage of streaming graphs, and ensure that both the space cost and the time cost of AnoD and AnoC for processing each new streaming edge remain constant and user-controllable, with estimation guarantees. Experimental evaluations on 6 real-world datasets demonstrate that the proposed evaluation metric and the associated algorithms achieve superior detection performance with clear interpretability for detecting anomalous interactions.
By mimicking the brain's efficient spiking encoding paradigm, spiking graph neural networks exhibit significant potential for efficient graph data analysis. Due to the inherent expressive limitations of binary spiking signals adopted in spiking encoding, existing models typically enhance their expression by integrating numerous real-valued multiplication-additions or high-latency encoding. However, such integrations compromise the core efficiency superiority of spiking models, limiting their scalability in real-world applications. To simultaneously reconcile considerable expression and efficiency, we propose E2SGNN, a novel network comprising a dual-scale modulated spiking backbone and a latency-dynamic optimization module. The former backbone integrates global and local real-valued graph modulations into spiking graph convolution, enabling discriminative dual-scale neighbor embedding in the encoding process. It both breaks through binary spiking signals' expressive limitations and improves the content expressiveness of spiking graph representations, while retaining low-latency and addition-only efficient advantages. Moreover, to further reduce the latency redundancy for higher efficiency, the latter module adaptively customizes the latency for each graph data based on data complexity. In this way, our network can finally generate graph representations expressively and efficiently. Experiments on various datasets demonstrate the superiority of our network in expression and efficiency.
Dynamic graph anomaly detection (DGAD) is essential for iden- tifying anomalies in evolving graphs across domains such as fi- nance and social networks. Recently, generalist graph anomaly detection (GAD) models have shown promising results. They are pretrained on multiple source datasets and generalize across do- mains. While effective on static graphs, they struggle to capture evolving anomalies in dynamic graphs. Moreover, the continuous emergence of new domains and the lack of labeled data further challenge generalist DGAD. Effective cross-domain DGAD requires both domain-specific and domain-agnostic anomalous patterns. Importantly, these patterns evolve temporally within and across domains. Building on these insights, we propose a DGAD model with Dynamic Prototypes (DP) to capture evolving domain-specific and domain-agnostic patterns. Firstly, DP-DGAD extracts dynamic prototypes, i.e., evolving representations of normal and anomalous patterns, from temporal ego-graphs and stores them in a memory buffer. The buffer is selectively updated to retain general, domain- agnostic patterns while incorporating new domain-specific ones. Then, an anomaly scorer compares incoming data with dynamic prototypes to flag both general and domain-specific anomalies. Fi- nally, DP-DGAD employs confidence detection guided memory buffer updating for effective adaptation to target domain. Extensive experiments demonstrate state-of-the-art performance across ten real-world datasets from different domains.
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.
How can we accurately classify graphs using only positive and unlabeled instances? This setting commonly arises in real-world tasks. For example, in drug side effect prediction, each drug is represented as a graph based on its chemical structure, and only those observed to cause adverse effects are labeled as positive. The remaining drugs are left unlabeled—not because they are confirmed safe, but because such side effects have not yet been discovered or reported. A challenge is that positive and negative graphs often share similar substructures, making it difficult for models to capture the subtle structural differences that distinguish true positives among unlabeled ones. This difficulty is further exacerbated by the absence of explicit negative labels, which leaves the model without clear guidance on how to separate informative from non-informative patterns. We propose Delta-PU, an accurate method for PU graph classification. Delta-PU performs dual reweighting: hop-level reweighting to emphasize informative substructures during pooling, and graph-level reweighting to suppress ambiguous unlabeled graphs. This dual mechanism allows the model to progressively focus on discriminative patterns while reducing the uncertainty introduced by unlabeled instances. Extensive experiments demonstrate that Delta-PU achieves the state-of-the-art performance in PU graph classification.
We consider a novel vertex cover problem on temporal graphs, where the edges in the graph may change over time, and a vertex selected into the solution has a lifespan d. Specifically, a vertex selected at time t can cover all incident edges in graphs from time slot t to t+d-1. This model effectively captures the scenario of monitoring communication links via secure nodes (monitors) with limited lifespan in a dynamic network. We provide a systematic study of this problem from both theoretical and practical perspectives. We analyze its computational complexity, develop approximation and online algorithms with tight ratios, and present a parameterized algorithm and a tight quadratic kernel under fixed d. Experimental results on random and real-world temporal networks demonstrate the effectiveness of our algorithms. We believe that our systematic study not only reveals the nature of the problem itself, but also paves the way for investigating the ''sustained'' version of other problems on temporal graphs.
A significant challenge in developing graph foundational models is to achieve universality and generalizability across general graphs, beyond text-attribute graphs. Unfortunately, most graph neural networks are designed for specific applications in particular fields and are difficult to generalize across different graphs. The most critical problem is the lack of Transfer Invariant Metadata (TIM) for graphs, akin to pixels for images and vocabularies for text, which prevents the development of graph foundational models. TIM is particularly problematic, albeit critical, on graph nodes with semantically variable features, making it difficult to transfer knowledge across graphs. Here, we analyze TIM and propose a theoretical approach to mining TIM in node features. It extracts semantically consistent information across domains and unifies data space with relatively low information loss. We then introduce a Transfer-Invariant Graph (TIG) foundational model to transform features of different dimensions into a unified structural representation. This transformation can effectively learn and extract TIM and intrinsic graph knowledge by self-supervised learning. We conducted extensive experiments, and the results showed that TIG even outperformed some models trained on data from targeted domains. In one-shot cross-domain scenarios, TIG achieved high accuracy with less training data and without any prompt tuning.