Disjoint and Non-Disjoint Community Detection with Control of Overlaps Between Communities

• Published in: SN computer science 2021-02, Vol.2 (1), p.15, Article 15
• Main Authors: Ben NCir, Chiheb-Eddine, Maiza, Ismail, Bouaguel, Waad, Essoussi, Nadia
• Format: Article
• Language: English
• Subjects: Collaboration; Computer Imaging; Computer Science; Computer Systems Organization and Communication Networks; Connectivity; Data analysis; Data Structures and Information Theory; Information Systems and Communication Service; Methods; Networks; Original Research; Pattern Recognition and Graphics; Software Engineering/Programming and Operating Systems; Sparsity; Vision
• ISSN: 2662-995X; 2661-8907
• DOI: 10.1007/s42979-020-00391-w

Overlapping community detection has become an important challenge in networks analysis that motivates researchers to propose community detection methods that best fit existing complex and non-disjoint structures in real-world networks such as social, scientific and collaborative networks. Existing overlapping community detection methods usually build large overlaps between communities, larger than expected, and do not allow users to interact with the system to regulate this size, except those allowing to include hard constraints. To solve these issues, we propose a novel non-disjoint community detection method, referred to as CDCO, which easily allows users to interact with the system and regulate overlaps between communities based on existing relationships between nodes in the network. In the same way that allowing to analysts to control the number of communities or the minimal number of actors in the community, CDCO allows to regulate overlaps using an α parameter which can favor or penalize overlaps. The regulation of overlaps is introduced in the objective criterion and optimized iteratively during the community detection process. Extensive experiments, conducted on both simulated and real-world networks having different sizes of overlaps, show the importance of the regulation of overlaps when a non-disjoint partitioning of the network is needed and show that CDCO outperforms existing conventional methods in terms of both F-measure and NMI.