Community structure: A comparative evaluation of community detection methods. (3rd March 2020)

• Record Type: Journal Article
• Authors: Dao, Vinh Loc; Bothorel, Cécile; Lenca, Philippe
• Is Part Of: Network science. Volume 8:Number 1(2020)
• Page Start: 1
• Page End: 41
• Publication Date: 2020-03-03
• Subjects: community detection, -- community structure, -- comparative analysis, -- empirical analysis, -- computation time, -- community size, -- structural quality function, -- validation metric, -- decision-making assistance for practitioners; Social networks -- Research -- Periodicals; System analysis -- Periodicals; System theory -- Periodicals; Computer science -- Periodicals; 003.72
• DOI: 10.1017/nws.2019.59
• Languages: English
• ISSNs: 2050-1242

Abstract

Abstract: Discovering community structure in complex networks is a mature field since a tremendous number of community detection methods have been introduced in the literature. Nevertheless, it is still very challenging for practitioners to determine which method would be suitable to get insights into the structural information of the networks they study. Many recent efforts have been devoted to investigating various quality scores of the community structure, but the problem of distinguishing between different types of communities is still open. In this paper, we propose a comparative, extensive, and empirical study to investigate what types of communities many state-of-the-art and well-known community detection methods are producing. Specifically, we provide comprehensive analyses on computation time, community size distribution, a comparative evaluation of methods according to their optimization schemes as well as a comparison of their partitioning strategy through validation metrics. We process our analyses on a very large corpus of hundreds of networks from five different network categories and propose ways to classify community detection methods, helping a potential user to navigate the complex landscape of community detection.