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