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 …
Self-Guided Robust Graph Structure Refinement
Recent studies have revealed that GNNs are vulnerable to adversarial attacks. To defend
against such attacks, robust graph structure refinement (GSR) methods aim at minimizing …
against such attacks, robust graph structure refinement (GSR) methods aim at minimizing …
Enhancing Fairness in Unsupervised Graph Anomaly Detection through Disentanglement
Graph anomaly detection (GAD) is increasingly crucial in various applications, ranging from
financial fraud detection to fake news detection. However, current GAD methods largely …
financial fraud detection to fake news detection. However, current GAD methods largely …
SmoothGNN: Smoothing-based GNN for Unsupervised Node Anomaly Detection
The smoothing issue leads to indistinguishable node representations, which poses a
significant challenge in the field of graph learning. However, this issue also presents an …
significant challenge in the field of graph learning. However, this issue also presents an …