  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.
