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

TL;DR

This paper presents a fast, statistically grounded method for extracting the backbone of projected bipartite networks, which simplifies community detection by filtering out less relevant edges.

Contribution

The paper introduces a computationally efficient backbone extraction technique based on Poisson binomial distribution, requiring only degree distributions, to improve community detection in large networks.

Findings

Backbone extraction improves community detection accuracy.

Method is computationally inexpensive and scalable.

Edge weights follow a Poisson binomial distribution.

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.