“…[79] GNN [172], [173] Graph-level Homogeneous GA [174]-[179] GNN [72], [73], [180], [181] Heterogeneous GA [182] Dynamic GA [183], [184] GNN [185] TABLE 3: Summary of anomaly detection on graphs.…”
Section: Discussionmentioning
confidence: 99%
“…Some studies consider graph-level anomaly detection on heterogeneous graphs. For instance, ACGPMiner [182] introduces conditional graph patterns to model abnormal patterns in property graphs and follows a generation-andvalidation paradigm to mine the defined abnormal patterns and their matches.…”
“…[79] GNN [172], [173] Graph-level Homogeneous GA [174]-[179] GNN [72], [73], [180], [181] Heterogeneous GA [182] Dynamic GA [183], [184] GNN [185] TABLE 3: Summary of anomaly detection on graphs.…”
Section: Discussionmentioning
confidence: 99%
“…Some studies consider graph-level anomaly detection on heterogeneous graphs. For instance, ACGPMiner [182] introduces conditional graph patterns to model abnormal patterns in property graphs and follows a generation-andvalidation paradigm to mine the defined abnormal patterns and their matches.…”
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