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InfoResearchPeer-reviewed
Homophily Edge Augment Graph Neural Network for High-Class Homophily Variance Learning
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Summary
The authors argue that high Class Homophily Variance (CHV) explains why vanilla GNNs perform poorly in graph anomaly detection, since benign nodes are highly homophilic while anomalies are not. They propose HEAug, which samples homophily adjacency matrices from scratch with a self-attention mechanism, connects feature-similar but unlinked nodes, and modifies the loss to penalize unnecessary heterophilic edges. HEAug achieved the best results across eight benchmark datasets.