Classification and Comparative Evaluation of Community Detection Algorithms

• Published in: Archives of computational methods in engineering Vol. 28; no. 3; pp. 1417 - 1428
• Main Authors: Mittal, Ruchi, Bhatia, M. P. S.
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.05.2021; Springer Nature B.V
• Subjects: Algorithms; Classification; Engineering; Mathematical and Computational Engineering; Original Paper; Social networks
• ISSN: 1134-3060; 1886-1784
• DOI: 10.1007/s11831-020-09421-5

The area of social networks has kept growing and exhibits multiple types of interaction among entities. This arrangement of nodes leads to the many kinds of studies in the social network. Community detection is one of the accessible areas in social networks which developed a significant interest among researchers. A community is defined as a deeply linked group of entities. In this paper, we target to present a survey on various types of communities and community detection algorithms in social networks. We also classify and evaluate the different community detection algorithms based on the base approach. Depending on the application and usage, this classification eases researchers to find a suitable community detection method for their work. We also present a comparative evaluation of many community detection algorithms on some popular social networks and demonstrate the performance of each of the algorithm.