Leveraging One-class Homophily for Unsupervised Graph Anomaly Detection

Normal nodes tend to have strong connection/affinity with each other, while the homophily in abnormal nodes is significantly weaker. Leveraging this one-class homophily property, we introduce a novel unsupervised anomaly scoring measure, local node affinity, and propose Truncated Affinity Maximization (TAM) to learn tailored node representations that maximize the local affinity of normal nodes for accurate graph anomaly detection.