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Online social networks (OSNs) are trendy and rapid information propagationmedium on the web where millions of new connections either positive such asacquaintance or negative such as animosity, are being established every dayaround the world. The negative links (or sometimes we can say harmfulconnections) are mostly established by fake profiles as they are being createdby minds with ill aims. Detecting negative (or suspicious) links within onlineusers can better aid in mitigation of fake profiles from OSNs. A modifiedclustering coefficient formula, named as Mutual Clustering Coefficientrepresented by M_cc, is introduced to quantitatively measure the connectivitybetween the mutual friends of two connected users in a group. In this paper, wepresent a classification system based on mutual clustering coefficient andprofile information of users to detect the suspicious links within the usercommunities. Profile information helps us to find the similarity between users.Different similarity measures have been employed to calculate the profilesimilarity between a connected user pair. Experimental results demonstrate thatfour basic and easily available features such aswork(w),education(e),home_town(ht)and current_city(cc) along with M_CC play avital role in designing a successful classification system for the detection ofsuspicious links.

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