Refinement for community structures of bipartite networks

TL;DR

This paper investigates how incorporating bipartivity constraints into community detection algorithms improves the robustness and reliability of identified community structures in bipartite networks, emphasizing the importance of customizing algorithms to network features.

Contribution

It demonstrates that community detection tailored to bipartivity yields more robust results, highlighting the need for customized algorithms that encode known network structural information.

Findings

Bipartivity-aware community detection is more reliable.

Customized algorithms improve community robustness.

Analysis on real and model networks supports the approach.

Abstract

Bipartite networks composed of dichotomous node sets are ubiquitous in nature and society. Partly for simplicity's sake, many studies have focused on their projection onto their unipartite versions where one only needs to care about a single type of node. When it comes to mesoscale structures such as communities, however, properly incorporating a priori structural restrictions such as bipartivity is ever more important. In this paper, as a case study, we take the community structure of bipartite networks in various scales to examine the amount of information of bipartivity encoded in the community detection procedure. In particular, we report the robustness in reliability of detected community based on consistency by comparing the detection algorithm with or without the consideration of bipartivity. From the analysis with model networks embedding prescribed communities and real networks, we find that the community detection tailored to take the bipartivity into account clearly yields more robust community structures than the one without such structural information. This demonstrates the necessity for customizing the community detection algorithm by encoding whatever information is known about networks of interest and, at the same time, raises an interesting question on the possibility of estimating the quantitative amount of information from such a customization.