Anomaly Detection in Temporal Graph Data: An Iterative Tensor Decomposition and Masking Approach

Sensors and Internet-of-Things scenarios promise a wealth of interaction data that can be naturally represented by means of timevarying graphs. This brings forth new challenges for the identification and removal of temporal graph anomalies that entail complex correlations of topological features and activity patterns. Here we present an anomaly detection approach for temporal graph data based on an iterative tensor decomposition and masking procedure. We test this approach using highresolution social network data from wearable sensors and show that it successfully detects anomalies due to sensor wearing time protocols.

• Type: Journal
• Year: 2015
• Where: PKDD
• Authors: Anna Sapienza, André Panisson, Joseph Wu, Laetitia Gauvin, Ciro Cattuto
• Keywords: Data Mining, PKDD 2015