  Graph-level anomaly detection is significant in diverse domains. To improve
detection performance, counterfactual graphs have been exploited to benefit the
generalization capacity by learning causal relations. Most existing studies
directly introduce perturbations (e.g., flipping edges) to generate
counterfactual graphs, which are prone to alter the semantics of generated
examples and make them off the data manifold, resulting in sub-optimal
performance. To address these issues, we propose a novel approach,
Motif-consistent Counterfactuals with Adversarial Refinement (MotifCAR), for
graph-level anomaly detection. The model combines the motif of one graph, the
core subgraph containing the identification (category) information, and the
contextual subgraph (non-motif) of another graph to produce a raw
counterfactual graph. However, the produced raw graph might be distorted and
cannot satisfy the important counterfactual properties: Realism, Validity,
Proximity and Sparsity. Towards that, we present a Generative Adversarial
Network (GAN)-based graph optimizer to refine the raw counterfactual graphs. It
adopts the discriminator to guide the generator to generate graphs close to
realistic data, i.e., meet the property Realism. Further, we design the motif
consistency to force the motif of the generated graphs to be consistent with
the realistic graphs, meeting the property Validity. Also, we devise the
contextual loss and connection loss to control the contextual subgraph and the
newly added links to meet the properties Proximity and Sparsity. As a result,
the model can generate high-quality counterfactual graphs. Experiments
demonstrate the superiority of MotifCAR.
