GADBench: Revisiting and Benchmarking Supervised Graph Anomaly Detection
This work addresses the need for comprehensive benchmarking in graph anomaly detection, providing a foundation for future research, though it is incremental as it focuses on evaluation rather than new methods.
The authors tackled the lack of standardized evaluation in supervised graph anomaly detection by introducing GADBench, a benchmark tool comparing 29 models on ten real-world datasets, finding that tree ensembles with simple neighborhood aggregation outperform the latest GNNs.
With a long history of traditional Graph Anomaly Detection (GAD) algorithms and recently popular Graph Neural Networks (GNNs), it is still not clear (1) how they perform under a standard comprehensive setting, (2) whether GNNs can outperform traditional algorithms such as tree ensembles, and (3) how about their efficiency on large-scale graphs. In response, we introduce GADBench -- a benchmark tool dedicated to supervised anomalous node detection in static graphs. GADBench facilitates a detailed comparison across 29 distinct models on ten real-world GAD datasets, encompassing thousands to millions ($\sim$6M) nodes. Our main finding is that tree ensembles with simple neighborhood aggregation can outperform the latest GNNs tailored for the GAD task. We shed light on the current progress of GAD, setting a robust groundwork for subsequent investigations in this domain. GADBench is open-sourced at https://github.com/squareRoot3/GADBench.