Prioritizing network communities
This addresses the need for efficient community prioritization in fields like biology and physics, where experimental validation is limited, though it is incremental as it builds on existing community detection methods.
The paper tackles the problem of prioritizing which network communities to select for downstream validation when only a few can be tested, by developing CRank, a method that evaluates robustness and magnitude of structural features, resulting in a nearly 50-fold improvement in prioritization.
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.