A comparative analysis of structural graph metrics to identify anomalies in online social networks

Abstract

Social networks are becoming vulnerable to a number of fraudulent attacks and mischievous activities due to their widespread use and increasing popularity. So, detection of anomalous activities, especially in social networks, is essentially required as it helps to identify important and significant information regarding the behavior of anomalous users. In order to detect anomalies in social networks, researchers have mainly relied on the use of behavior and structure based approaches. Working in the similar direction, we extend the graph structure based approach by introducing and analyzing important graph metrics to detect anomalous activities. The comparison and effectiveness of measures have been presented on the basis of statistical measures like precision, recall and F-score, as well as on the basis of calculated anomalous scores. Theoretical and empirical evaluation reveals that the relationship between brokerage and number of edges helps to detect and correctly rank maximum number of anomalies.