Graph based Unsupervised Learning Methods for Edge and Node Anomaly Detection in Social Network

Abstract

In the last decade online social networks analysis has become an interesting area of research for researchers, to study and analyze the activities of users using which the user interaction pattern can be identified and capture any anomalies within an user community. Detecting such users can help in identifying malicious individuals such as automated bots, fake accounts, spammers, sexual predators, and fraudsters. An anomaly (outliers, deviant patterns, exceptions, abnormal data points, malicious user) is an important task in social network analysis. The major hurdle in social networks anomaly detection is to identify irregular patterns in data that behaves significantly different from regular patterns. The focus of this paper is to propose graph based unsupervised machine learning methods for edge anomaly and node anomaly detection in social network data. © 2019 IEEE.