2018/05/01 by Mudasir Ahmad Wani, Wani, Mudasir Ahmad, Suraiya Jabin +1
Computer Science · Physics and Astronomy · Social Sciences · #Complex Network Analysis Techniques #FOS: Computer and information sciences #Misinformation and Its Impacts #Social and Information Networks (cs.SI) #Spam and Phishing Detection
paper · pdf · doi:10.48550/arxiv.1805.00537
openalex publication_date 2018/05/01 · openalex created_date 2022/10/01 · openalex updated_date 2026/07/28
Online social networks (OSNs) are trendy and rapid information propagation\nmedium on the web where millions of new connections either positive such as\nacquaintance or negative such as animosity, are being established every day\naround the world. The negative links (or sometimes we can say harmful\nconnections) are mostly established by fake profiles as they are being created\nby minds with ill aims. Detecting negative (or suspicious) links within online\nusers can better aid in mitigation of fake profiles from OSNs. A modified\nclustering coefficient formula, named as Mutual Clustering Coefficient\nrepresented by Mcc, is introduced to quantitatively measure the connectivity\nbetween the mutual friends of two connected users in a group. In this paper, we\npresent a classification system based on mutual clustering coefficient and\nprofile information of users to detect the suspicious links within the user\ncommunities. Profile information helps us to find the similarity between users.\nDifferent similarity measures have been employed to calculate the profile\nsimilarity between a connected user pair. Experimental results demonstrate that\nfour basic and easily available features such as\nwork(w),education(e),hometown(ht)and currentcity(cc) along with MCC play a\nvital role in designing a successful classification system for the detection of\nsuspicious links.\n