NK-GAD improves unsupervised graph anomaly detection on heterophilic graphs by combining a joint encoder for similar and dissimilar neighbors, neighbor reconstruction, center aggregation, and dual decoders, yielding an average 3.29% AUC gain across seven datasets.
In: 2024 International Conference on Science Technology Engineering and Management (ICSTEM)
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NK-GAD: Neighbor Knowledge-Enhanced Unsupervised Graph Anomaly Detection
NK-GAD improves unsupervised graph anomaly detection on heterophilic graphs by combining a joint encoder for similar and dissimilar neighbors, neighbor reconstruction, center aggregation, and dual decoders, yielding an average 3.29% AUC gain across seven datasets.