DeNoise: Learning Robust Graph Representations for Unsupervised Graph-Level Anomaly Detection

Excerpt

arXiv:2511.04086v2 Announce Type: replace-cross Abstract: With the rapid growth of graph-structured data in critical domains, unsupervised graph-level anomaly detection (UGAD) has become a pivotal task. UGAD seeks to identify entire graphs that deviate from normal behavioral patterns. However, most Graph Neural Network (GNN) approaches implicitly assume that the training set is clean, containing only normal graphs, which is rarely true in practice. Even modest contamination by anomalous graphs c