2019/08/07 by Rudy Arthur · 16 citations
Computer Science · Mathematics · Physics and Astronomy · #Advanced Clustering Algorithms Research #Algorithm #Artificial intelligence #Bipartite graph #Caching and Content Delivery #Clique percolation method #Combinatorics #Complex Network Analysis Techniques #Complex network #Computer science #Function (biology) #Graph #Heuristic #Mathematics #Modularity (biology) #Partition (number theory) #Projection (relational algebra) #Set (abstract data type) #Theoretical computer science #cs.SI #physics.soc-ph
paper · pdf · open access · doi:10.1016/j.physa.2020.124341
published in Physica A Statistical Mechanics and its Applications 549, 124341 (Elsevier BV)
arxiv created 2019/08/07 · openalex publication_date 2020/02/15 · arxiv updated 2020/05/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
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 heuristic for finding communities in bipartite networks.