Prioritizing network communities

TL;DR

This paper introduces CRank, a method for prioritizing network communities based on structural features, significantly improving the selection process for experimental validation in various scientific fields.

Contribution

CRank provides a mathematically principled, efficient approach to rank communities using only network structure, adaptable with domain-specific information when available.

Findings

CRank achieves nearly 50-fold improvement in community prioritization.

It is compatible with any community detection method and requires no additional metadata.

CRank effectively identifies robust and significant communities in large networks.

Abstract

Uncovering modular structure in networks is fundamental for systems in biology, physics, and engineering. Community detection identifies candidate modules as hypotheses, which then need to be validated through experiments, such as mutagenesis in a biological laboratory. Only a few communities can typically be validated, and it is thus important to prioritize which communities to select for downstream experimentation. Here we develop CRank, a mathematically principled approach for prioritizing network communities. CRank efficiently evaluates robustness and magnitude of structural features of each community and then combines these features into the community prioritization. CRank can be used with any community detection method. It needs only information provided by the network structure and does not require any additional metadata or labels. However, when available, CRank can incorporate domain-specific information to further boost performance. Experiments on many large networks show that CRank effectively prioritizes communities, yielding a nearly 50-fold improvement in community prioritization.