Mutual clustering coefficient-based suspicious-link detection approach for online social networks

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 animosity, being established every day around world. The links (or sometimes referred to harmful connections) mostly by fake profiles they created minds with ill aims. Detecting suspicious) within online users can better aid in mitigation from OSNs. A modified clustering coefficient formula, named MutualClusteringCoefficient represented byMcc, is introduced quantitatively measure connectivity between mutual friends two connected a group. In this paper, classification system based profile has been presented detect suspicious user communities. Profile helps us find similarity users. Different measures have employed calculate pair. Experimental results demonstrate that four basic easily available features workw,educatione,home_townhtandcurrent_city(cc) along MCC play vital role designing successful for detection links.