AD-C: A New Node Anomaly Detection based on Community Detection in Social Networks

Abstract

Anomaly detection in social networks as a challenging task has gained great attention. Every unusual behavioural pattern in a social network can be spotted as an anomaly which provides useful information. In this paper, a new method is proposed to identify anomaly based on community detection (AD-C) for the social network graph. Our model is made up of weighting in pre-processing step and three principle processes, including community detection, auxiliary community detection and node filtering. AD-C method offers a flexible framework for anomaly detection, which can be employed in different stages of its related algorithms. The experiments are conducted on two social media datasets, including Facebook and Flickr datasets. Experimental results indicate more efficiency in comparison to other anomaly methods as baselines in terms of the F-score. Also, the results indicate that applying the proposed steps lead to increased accuracy of the community detection methods.