Truncated Affinity Maximization: One-class Homophily Modeling for Graph Anomaly Detection

arXiv:2306.00006v5 Announce Type: replace-cross
Abstract: We reveal a one-class homophily phenomenon, which is one prevalent property we find empirically in real-world graph anomaly detection (GAD) datasets, i.e., normal nodes tend to have strong connection/affinity with each other, while the homophily in abnormal nodes is significantly weaker than normal nodes. However, this anomaly-discriminative property is ignored by existing GAD methods that are typically built using a conventional anomaly detection objective, such as data reconstruction. In this work, we explore this property …

anomaly anomaly detection arxiv class cs.ai cs.lg cs.si detection graph modeling type