A semi-supervised graph-based algorithm for detecting outliers in online-social-networks
Reza Hassanzadeh, Richi Nayak · 2013
In this paper, we propose a semi-supervised approach of anomaly detection in Online Social Networks. The social network is modeled as a graph and its features are extracted to detect anomaly. A clustering algorithm is then used to group users based on these features and fuzzy logic is applied to assign degree of anomalous behavior to the users of these clusters. Empirical analysis shows effectiveness of this method.