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
outperform traditional algorithms such as tree ensembles, and (3) their
efficiency on large-scale graphs. In response, we present GADBench -- a
comprehensive benchmark for supervised anomalous node detection on static
graphs. GADBench provides a thorough comparison across 23 distinct models on
ten real-world GAD datasets ranging from thousands to millions of nodes
($\sim$6M). Our main finding is that tree ensembles with simple neighborhood
aggregation outperform all other baselines, including the latest GNNs tailored
for the GAD task. By making GADBench available as an open-source tool, we offer
pivotal insights into the current advancements of GAD and establish a solid
foundation for future research. Our code is available at
this https URL

我们提供了 GADBench - 一个静态图中监督异常节点检测的全面基准测试。我们的主要发现是，使用简单邻域聚合的树集成优于所有其他基线，包括针对 GAD 任务量身定制的最新 GNN。