Community Detection in Bipartite Network: A Modified Coarsening Approach

• Published in: Information Management and Big Data Vol. 795; pp. 123 - 136
• Main Authors: Valejo, Alan, Ferreira, Vinícius, de Oliveira, Maria C. F., de Andrade Lopes, Alneu
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2018; Springer International Publishing
• Series: Communications in Computer and Information Science
• ISBN: 9783319905952; 3319905953
• ISSN: 1865-0929; 1865-0937
• DOI: 10.1007/978-3-319-90596-9_9

Interest in algorithms for community detection in networked systems has increased over the last decade, mostly motivated by a search for scalable solutions capable of handling large-scale networks. Multilevel approaches provide a potential solution to scalability, as they reduce the cost of a community detection algorithm by applying it to a coarsened version of the original network. The solution obtained in the small-scale network is then projected back to the original large-scale model to obtain the desired solution. However, standard multilevel methods are not directly applicable to bipartite networks and there is a gap in existing literature on multilevel optimization applied to such networks. This article addresses this gap and introduces a novel multilevel method based on one-mode projection that allows executing traditional multilevel methods in bipartite network models. The approach has been validated with an algorithm for community detection that solves the Barber’s modularity problem. We show it can scale a target algorithm to handling larger networks, whilst preserving solution accuracy.