Mutual Clustering Coefficient-based Suspicious-link Detection approach for Online Social Networks

TL;DR

This paper introduces a novel mutual clustering coefficient-based method combined with profile similarity measures to detect suspicious or harmful links in online social networks, aiming to identify fake profiles and malicious connections.

Contribution

The paper proposes a new mutual clustering coefficient metric and integrates it with profile information to improve detection of suspicious links in OSNs.

Findings

Mutual Clustering Coefficient (M_cc) effectively measures user connectivity.

Profile features like work, education, hometown, and current city enhance detection accuracy.

Combined features outperform individual measures in classifying suspicious links.

Abstract

Online social networks (OSNs) are trendy and rapid information propagation medium on the web where millions of new connections either positive such as acquaintance or negative such as animosity, are being established every day around the world. The negative links (or sometimes we can say harmful connections) are mostly established by fake profiles as they are being created by minds with ill aims. Detecting negative (or suspicious) links within online users can better aid in mitigation of fake profiles from OSNs. A modified clustering coefficient formula, named as Mutual Clustering Coefficient represented by M_cc, is introduced to quantitatively measure the connectivity between the mutual friends of two connected users in a group. In this paper, we present a classification system based on mutual clustering coefficient and profile information of users to detect the suspicious links within the user communities. Profile information helps us to find the similarity between users. Different similarity measures have been employed to calculate the profile similarity between a connected user pair. Experimental results demonstrate that four basic and easily available features such as work(w),education(e),home_town(ht)and current_city(cc) along with M_CC play a vital role in designing a successful classification system for the detection of suspicious links.