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Graph Anomaly Detection (GAD) has recently become a hot research spot due toits practicability and theoretical value. Since GAD emphasizes the applicationand the rarity of anomalous samples, enriching the varieties of its datasets isfundamental work. Thus, this paper present DGraph, a real-world dynamic graphin the finance domain. DGraph overcomes many limitations of current GADdatasets. It contains about 3M nodes, 4M dynamic edges, and 1M ground-truthnodes. We provide a comprehensive observation of DGraph, revealing thatanomalous nodes and normal nodes generally have different structures, neighbordistribution, and temporal dynamics. Moreover, it suggests that unlabeled nodesare also essential for detecting fraudsters. Furthermore, we conduct extensiveexperiments on DGraph. Observation and experiments demonstrate that DGraph ispropulsive to advance GAD research and enable in-depth exploration of anomalousnodes.

DGraph: A Large-Scale Financial Dataset for Graph Anomaly Detection