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.
Node-Level Graph Anomaly Detection: Outliers, Message Passing, and Hyperbolic Neural Networks
Graph anomaly detection methods are enhanced by novel outlier injection techniques, message passing analysis, and the utilization of hyperbolic neural networks.
Generative Semi-supervised Graph Anomaly Detection: Exploiting Normal Nodes for Enhanced Anomaly Detection
Utilizing normal nodes enhances anomaly detection performance in a semi-supervised setting.
Analyzing Node-Level Graph Anomaly Detection Methods
The author revisits node-level graph anomaly detection methods, focusing on outlier injection techniques, message passing impact, and hyperbolic neural networks to enhance performance.
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