  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.
