SocioRank*: A community and role detection method in social networks. (June 2019)

• Record Type: Journal Article
• Authors: Rafique, Wajid; Khan, Maqbool; Sarwar, Nadeem; Dou, Wanchun
• Is Part Of: Computers & electrical engineering. Volume 76(2019)
• Page Start: 122
• Page End: 132
• Publication Date: 2019-06
• Subjects: Social network analysis -- Role discovery -- Behavior -- Community detection -- Betweenness -- Graph metrics; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2019.03.010
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: Due to the widespread popularity of cyber-social networks, research on community and social role discovery has drawn immense attention. Most of the available literature consider community and social role detection as areas of a disjoint problem domain. Moreover, existing research on concurrent community and social role detection utilizes a predefined set of roles and lacks in providing enough ground truth information for the proof. Hence, there is a high need for community structure analysis from an independent social perspective. Therefore, we propose a community and social role detection method called SocioRank* using email interaction data crawled from a graduate class of students. SocioRank* detects the underlying communities and ranks individuals on the basis of degree, closeness, and betweenness centrality. We perform extensive graph metrics-based experiments to evaluate the effectiveness of the proposed method. The comparison of results with the ground truth information demonstrates the high accuracy of SocioRank* method in community and role discovery.