Outlier and anomalous behavior detection in social networks using constraint programming

Abstract

Outlier and anomaly detection are widely used in several fields of study such as social networks, statistics, and knowledge discovery. In social networks, it is useful to detect structural abnormalities which are different from the typical behavior of the social network in order to maintain the network security and privacy. In this paper, we suggest a new approach for outlier and anomalous behavior detection in social networks based on combined graph pattern matching and constraint programming. For this purpose, we utilize the Constraint Programming (CP) techniques for matching the original graph data with the graph pattern data, to detect two formalized anomalies: anomalous nodes and anomalous edges. We also introduce a neighborhood constraint formalization that aims to precise the anomaly that replaced specific node as well as the changes that it made within the networks. Finally, we present our experimental results that show the effectiveness and efficiency of our approach in terms of computational time and matching accuracy.