Modularity and Projection of Bipartite Networks
arXiv:1908.02520 · doi:10.1016/j.physa.2020.124341
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 heuristic for finding communities in bipartite networks.
References in corpus (25)
- Emergence of scaling in random networks
- Fast unfolding of communities in large networks
- Community structure in social and biological networks
- Community detection in graphs
- Fast algorithm for detecting community structure in networks
- Uncovering the overlapping community structure of complex networks in nature and society
- The structure of scientific collaboration networks
- Random graphs with arbitrary degree distributions and their applications
- Benchmark graphs for testing community detection algorithms
- Resolution limit in community detection
- Who is the best connected scientist? A study of scientific coauthorship networks
- Statistical Mechanics of Community Detection
- How to project a bipartite network?
- The performance of modularity maximization in practical contexts
- Modularity from Fluctuations in Random Graphs and Complex Networks
- Modularity and community detection in bipartite networks
- The ground truth about metadata and community detection in networks
- Spectral methods for network community detection and graph partitioning
- Phase transition in the detection of modules in sparse networks
- Graph spectra and the detectability of community structure in networks
- Bi-clique Communities
- Finding multiple core-periphery pairs in networks
- (Un)detectable cluster structure in sparse networks
- Degree distributions of bipartite networks and their projections
- Understanding how both the partitions of a bipartite network affect its one-mode projection