Generation is better than Modification: Combating High Class Homophily Variance in Graph Anomaly Detection

Understand

Graph-based anomaly detection is currently an important research topic in the field of graph neural networks (GNNs).

• We find that in graph anomaly detection, the homophily distribution differences between different classes are significantly greater than those in homophilic and heterophilic graphs.
• For the first time, we introduce a new metric called Class Homophily Variance, which quantitatively describes this phenomenon.
• To mitigate its impact, we propose a novel GNN model named Homophily Edge Generation Graph Neural Network (HedGe).