GCN-based graph anomaly detection

Xu Zhang; XueBin Sun

doi:10.1117/12.2631866

14 April 2022 GCN-based graph anomaly detection

Xu Zhang, XueBin Sun

Proceedings Volume 12178, International Conference on Signal Processing and Communication Technology (SPCT 2021); 1217808 (2022) https://doi.org/10.1117/12.2631866
Event: International Conference on Signal Processing and Communication Technology (SPCT 2021), 2021, Tianjin, China

Abstract

Graph data structures are now widely used and detecting graph anomalies is a challenging task. Traditional anomaly detection methods can achieve good results in the case of low-latitude data, but in the face of today's unstructured graph structure data, they often seem to be powerless. Fortunately,GCN provides an effective method for processing graph data. Based on the idea of GCN network and traditional support vector data description, this paper proposes a graph anomaly detection method and makes corresponding experiments. Compared with traditional methods, better results have been achieved.

Citation Download Citation

Xu Zhang and XueBin Sun "GCN-based graph anomaly detection", Proc. SPIE 12178, International Conference on Signal Processing and Communication Technology (SPCT 2021), 1217808 (14 April 2022); https://doi.org/10.1117/12.2631866

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