  Different from the current node-level anomaly detection task, the goal of
graph-level anomaly detection is to find abnormal graphs that significantly
differ from others in a graph set. Due to the scarcity of research on the work
of graph-level anomaly detection, the detailed description of graph-level
anomaly is insufficient. Furthermore, existing works focus on capturing
anomalous graph information to learn better graph representations, but they
ignore the importance of an effective anomaly score function for evaluating
abnormal graphs. Thus, in this work, we first define anomalous graph
information including node and graph property anomalies in a graph set and
adopt node-level and graph-level information differences to identify them,
respectively. Then, we introduce a discriminative graph-level anomaly detection
framework with dual-students-teacher model, where the teacher model with a
heuristic loss are trained to make graph representations more divergent. Then,
two competing student models trained by normal and abnormal graphs respectively
fit graph representations of the teacher model in terms of node-level and
graph-level representation perspectives. Finally, we combine representation
errors between two student models to discriminatively distinguish anomalous
graphs. Extensive experiment analysis demonstrates that our method is effective
for the graph-level anomaly detection task on graph datasets in the real world.
