A classification of community detection methods in social networks: a survey. Issue 1 (2nd January 2021)

• Record Type: Journal Article
• Authors: Souravlas, S.; Sifaleras, A.; Tsintogianni, M.; Katsavounis, S.
• Is Part Of: International journal of general systems. Volume 50:Issue 1(2021)
• Page Start: 63
• Page End: 91
• Publication Date: 2021-01-02
• Subjects: Community detection -- bottom-up -- top-down -- data structures; System theory -- Periodicals; 003
• DOI: 10.1080/03081079.2020.1863394
• Languages: English
• ISSNs: 0308-1079

Abstract

ABSTRACT: The detection of community structures is a crucial research area. The problem of community detection has received considerable attention from a large portion of the scientific community and a very large number of papers has already been published in the literature. Even more important is the fact that, this large number of articles is in fact spread across a large number of different disciplines, from computer science, to statistics, and social sciences. These facts necessitate some type of classification and organization of these works. In this work, our basic classification approach divides the community detection schemes into three basic approaches: (a) the bottom-up approaches, (b) the top-down approaches, and (c) the data structure-based approaches. The first category includes the majority of algorithms, so further classification is possible. Such a classification is included in this work. For the other two categories, we make no further categorizations but we simply focus our discussion on the metrics or the data structures being used. Finally, a few possible directions for future research are also suggested.