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Anomaly detection in networks with temporal information

Abstract:

We present a technique for node anomaly detection in networks where arcs are annotated with time of creation. The technique aims at singling out anomalies by taking simultaneously into account information concerning both the structure of the network and the order in which connections have been established. The latter information is obtained by timestamps associated with arcs. A set of temporal structures is induced by checking certain conditions on the order of arc appearance denoting different kinds of user behaviors. The distribution of these structures is computed for each node and used to detect anomalies. The anomaly score measures the deviation from the expected number of structures associated with each node on the basis of the correlation between nodes degree and numerousness of exhibited structures. The resulting algorithm has low computational cost and is applicable to large networks. We present experimental results on some real-life networks showing the reliability of the approach.