Generic Anomalous Vertices Detection Utilizing a Link Prediction Algorithm

TL;DR

This paper introduces an unsupervised two-layered meta-classifier that effectively detects anomalous vertices in complex networks using topology features, outperforming existing methods in accuracy and efficiency across various network scales.

Contribution

The study presents a novel unsupervised meta-classifier for anomaly detection in networks, leveraging topology features and demonstrating superior performance over existing approaches.

Findings

Lower false positive rates in anomaly detection.

Higher AUC scores compared to prevalent methods.

Effective in identifying fake users and influential individuals.

Abstract

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.