The paper introduces JPGFN, a graph anomaly detection method that enhances frequency-domain filtering with a Feature Separation Transformation Network to capture fine-grained node features, an adaptive Jacobi polynomial graph filtering module to better model complex frequency-domain characteristics, and a node label constraint module to leverage label information. These components address limitations of static filters, attribute importance neglect, and insufficient label use found in existing approaches. Experiments on real-world datasets show that JPGFN outperforms mainstream methods.
arXiv:2606. 12673v1 Announce Type: cross Abstract: Cross-domain graph anomaly detection (GAD) aims to identify abnormal nodes in unseen target graphs, showing strong potential in real-world applications with heterogeneous graph data.
FoundAna is a GNN‑assisted foundation model designed for graph anomaly detection across diverse datasets. It combines a GNN component with a transformer encoder enhanced by four positional encodings to capture both local and global structure, using reconstruction errors as anomaly scores. Experiments on nine benchmark datasets from financial, social, and citation networks show that FoundAna consistently outperforms state‑of‑the‑art baselines.
arXiv:2505. 18934v2 Announce Type: replace-cross Abstract: Graph Anomaly Detection (GAD) in heterogeneous networks presents unique challenges due to node and edge heterogeneity.
arXiv:2505. 21285v5 Announce Type: replace Abstract: This work proposes a framework LGKDE that learns kernel density estimation for graphs.
arXiv:2511. 22078v2 Announce Type: replace Abstract: Many real-world scenarios involving streaming information can be represented as temporal graphs, where data flows through dynamic changes in edges over time.
arXiv:2609.15483v1 Announce Type: new Abstract: Unsupervised multivariate time series anomaly detection methods typically identify anomalies through forecasting, reconstruction, or representation dis...
arXiv:2511. 17113v3 Announce Type: replace-cross Abstract: Network Intrusion Detection Systems (NIDS) are essential tools for detecting network attacks and intrusions.
arXiv:2510. 02014v3 Announce Type: replace Abstract: Graph anomaly detection (GAD) has attracted growing interest for its crucial ability to uncover irregular patterns in broad applications.
arXiv:2606. 00304v1 Announce Type: new Abstract: Graph anomaly detection methods aim to distinguish anomalous nodes.
arXiv:2602.06859v3 Announce Type: replace-cross Abstract: Graph Anomaly Detection (GAD) aims to identify irregular patterns in graph data, and recent works have explored zero-shot generalist GAD to e...
The paper introduces an unsupervised hypergraph neural network designed to detect anomalous hyperedges—higher-order associations that deviate from typical patterns. Unlike conventional graph methods that capture only pairwise relationships, this approach leverages hypergraphs to model associations among any number of entities. Experiments on real-life datasets show the model effectively identifies unusual hyperedges without requiring labeled data.
arXiv:2605.26857v2 Announce Type: replace Abstract: Driven by the pressing demand for graph anomaly detection (GAD) in high-stakes domains, the generalist GAD paradigm, which trains a single detector...