STRUCTURAL GRAPH NEURAL NETWORKS FOR SUSPICIOUS EVENT DETECTION

摘要

A computer-implemented method for graph structure based anomaly detection on a dynamic graph is provided. The method includes detecting anomalous edges in the dynamic graph by learning graph structure changes in the dynamic graph with respect to target edges to be evaluated in a given time window repeatedly applied to the dynamic graph. The target edges correspond to particular different timestamps. The method further includes predicting a category of each of the target edges as being one of anomalous and non-anomalous based on the graph structure changes. The method also includes controlling a hardware based device to avoid an impending failure responsive to the category of at least one of the target edges.