Fast extraction of the backbone of projected bipartite networks to aid community detection

Abstract

This paper introduces a computationally inexpensive method of extracting the backbone of one-mode networks projected from bipartite networks. We show that the edge weights in the one-mode projections are distributed according to a Poisson binomial distribution and that finding the expected weight distribution of a one-mode network projected from a random bipartite network only requires knowledge of the bipartite degree distributions. Being able to extract the backbone of a projection is highly beneficial in filtering out redundant information in large complex networks and narrowing down the information in the one-mode projection to the most relevant. We demonstrate that the backbone of a one-mode projection aids in the detection of communities.

References in corpus (4)

• Finding community structure in networks using the eigenvectors of matrices
• Extracting the multiscale backbone of complex weighted networks
• Identifying Influential Nodes in Bipartite Networks Using the Clustering Coefficient
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Cited by in corpus (7)

• backbone: An R Package for extracting the backbone of bipartite projections
• Comparing Alternatives to the Fixed Degree Sequence Model for Extracting the Backbone of Bipartite Projections
• An Evaluation Tool for Backbone Extraction Techniques in Weighted Complex Networks
• Irreducible network backbones: unbiased graph filtering via maximum entropy
• Associative nature of event participation dynamics: a network theory approach
• Extracting the signed backbone of intrinsically dense weighted networks
• Detectability threshold in weighted modular networks