Generic Anomalous Vertices Detection Utilizing a Link Prediction   Algorithm
  In the past decade, network structures have penetrated nearly every aspect of our lives. The detection of anomalous vertices in these networks has become increasingly important, such as in exposing computer network intruders or identifying fake online reviews. In this study, we present a novel unsupervised two-layered meta-classifier that can detect irregular vertices in complex networks solely by using features extracted from the network topology. Following the reasoning that a vertex with many improbable links has a higher likelihood of being anomalous,we employed our method on 10 networks of various scales, from a network of several dozen students to online social networks with millions of users. In every scenario, we were able to identify anomalous vertices with lower false positive rates and higher AUCs compared to other prevalent methods. Moreover, we demonstrated that the presented algorithm is efficient both in revealing fake users and in disclosing the most influential people in social networks. 
Dima Kagan, Yuval Elovici, and Michael Fire
cs.SI physics.soc-ph