Detection of misbehaving individuals in social networks using overlapping communities and machine learning

Abstract

Detecting misbehavior in social networks is essential for maintaining trust and reliability in online communities. Traditional methods of identification often rely on individual attributes or structural network properties, which may overlook subtle or complex misbehavior patterns. This paper introduces a novel approach called OCMLMD that leverages network overlapping community structure and machine learning techniques to detect misbehavior. Our method combines graph-based analyses of network topology with state-of-theart machine learning algorithms to identify suspicious behavior indicative of misbehavior. Specifically, we target nodes that belong to multiple communities or exhibit weak connections within their community, utilizing a novel metric for selecting overlapping nodes. Additionally, we develop a machine learning model trained on relevant attributes extracted from social network data to detect misbehavior accurately. Extensive experiments on synthetic and real-world social network datasets demonstrate the superior performance of OCMLMD compared to baseline methods. Overall, our proposed approach offers a promising solution to the challenge of detecting misbehavior in social networks.

Keywords

• Social network
• Graph mining
• Overlapping community
• Contextual anomaly
• Structural anomaly
• Anomaly detection