Modularity and projection of bipartite networks

Rudy Arthur

Physica A: Statistical Mechanics and its Applications, 2020, vol. 549, issue C

Abstract: This paper investigates community detection by modularity maximisation on bipartite networks. In particular we are interested in how the operation of projection, using one node set of the bipartite network to infer connections between nodes in the other set, interacts with community detection. We first define a notion of modularity appropriate for a projected bipartite network and outline an algorithm for maximising it in order to partition the network. Using both real and synthetic networks we compare the communities found by five different algorithms, where each algorithm maximises a different modularity function and sees different aspects of the bipartite structure. Based on these results we suggest a simple ‘rule of thumb’ for finding communities in bipartite networks.

Keywords: Networks; Community detection; Bipartite; Modularity (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:549:y:2020:i:c:s0378437120301151

DOI: 10.1016/j.physa.2020.124341

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