Graph Anomaly Detection via Feature Selection with Local Topological Residuals

Local anomaly detection via subgraph embedding on multivariate data streams

The results indicate that entropy-based subgraph embedding can improve local anomaly detection performance, although the model does not achieve the best value for every metric on every dataset.

Generate and Filter: A GNN-Based Approach for Graph Anomaly Detection.

A novel framework, Generate and Filter graph learning for Graph Anomaly Detection (GFGAD), which generates a diverse set of synthetic anomalies with enriched feature and structural information to balance the data distribution and significantly outperforms state-of-the-art baselines.

Feature Transformation Enhanced Jacobi Polynomial Graph Filtering for Graph Anomaly Detection

A novel graph anomaly detection method called JPGFN (Feature Transformation Enhanced Jacobi Polynomial Graph Filtering Network), which significantly outperforms mainstream approaches on multiple real-world datasets.

Unsupervised Anomaly Detection in Dynamic Graphs via Compatibility Modeling and Boundary Learning

BAD is proposed, an unsupervised framework for anomaly detection in continuous-time dynamic graphs that represents nodes with learnable identity embeddings and performs pairwise compatibility modeling via cross-attention between each destination node and the source’s recent neighbors, enabling direct characterization of context-dependent deviations without requiring attributes.

PRA-TAM: prototype-regularised residual affinity maximisation for unsupervised graph anomaly detection

Graph anomaly detection plays a critical role in identifying irregular patterns in complex networked data arising in domains such as social networks, e-commerce systems, and cybersecurity. Existing approaches, particularly affinity-based methods, have demonstrated promising performance by leveraging local neighbourhood consistency. However, they often rely on a single anomaly indicator and lack an explicit mechanism to model normal behaviour, limiting their ability to detect subtle, heterogeneous anomalies. To address these challenges, this paper proposes a novel framework, prototype-regularised residual affinity maximisation (PRA-TAM), for unsupervised graph anomaly detection. The proposed method extends affinity-based learning by introducing a prototype-guided normality modelling mechanism that captures dominant patterns of normal nodes in the latent space using a compact set of learnable prototypes. In addition, a residual inconsistency calibration strategy is developed to quantify deviations across the feature, embedding, and neighbourhood spaces, enabling a more comprehensive assessment of node abnormality. To further enhance robustness, a lightweight multi-view learning strategy based on fixed graph truncation is employed to capture structural variations without introducing additional computational complexity. Extensive experiments across multiple benchmark datasets, including Facebook, ACM, Amazon, and YelpChi, demonstrate that the proposed method achieves competitive AUROC and AUPRC performance while demonstrating robust performance across multiple benchmark datasets and remains competitive on YelpChi. The results highlight the effectiveness of integrating affinity learning with prototype modelling and residual-based scoring for improved anomaly detection performance. The proposed framework is computationally efficient, scalable, and well-suited to real-world graph anomaly detection applications characterised by complex, heterogeneous data distributions.

Unsupervised Graph-level Anomaly Detection via Multi-granular Graph Structure Learning