Deep graph anomaly detection: A survey and new perspectives
Graph anomaly detection (GAD), which aims to identify unusual graph instances (nodes,
edges, subgraphs, or graphs), has attracted increasing attention in recent years due to its …
edges, subgraphs, or graphs), has attracted increasing attention in recent years due to its …
DiffGAD: A Diffusion-based Unsupervised Graph Anomaly Detector
Graph Anomaly Detection (GAD) is crucial for identifying abnormal entities within networks,
garnering significant attention across various fields. Traditional unsupervised methods …
garnering significant attention across various fields. Traditional unsupervised methods …
Graph anomaly detection based on hybrid node representation learning
X Wang, H Dou, D Dong, Z Meng - Neural Networks, 2025 - Elsevier
Anomaly detection on graph data has garnered significant interest from both the academia
and industry. In recent years, fueled by the rapid development of Graph Neural Networks …
and industry. In recent years, fueled by the rapid development of Graph Neural Networks …
Graph Structure Refinement with Energy-based Contrastive Learning
Graph Neural Networks (GNNs) have recently gained widespread attention as a successful
tool for analyzing graph-structured data. However, imperfect graph structure with noisy links …
tool for analyzing graph-structured data. However, imperfect graph structure with noisy links …