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 such as animosity, are being established every day around the world. The negative links (or sometimes referred to as harmfu...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Springer
2022-02-01
|
| Series: | Journal of King Saud University: Computer and Information Sciences |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1319157818304208 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | 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 referred to as 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 the mitigation of fake profiles from OSNs.A modified clustering coefficient formula, named as MutualClusteringCoefficient represented byMcc, is introduced to quantitatively measure the connectivity between the mutual friends of two connected users in a group. In this paper, a classification system based on mutual clustering coefficient and profile information of users has been presented 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 workw,educatione,home_townhtandcurrent_city(cc) along with MCC play a vital role in designing a successful classification system for the detection of suspicious links. |
|---|---|
| ISSN: | 1319-1578 |