Prioritizing network communities

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 CR ank , a mathematically principled approach for prioritizing network communities. CR ank efficiently evaluates robustness and magnitude of structural features of each community and then combines these features into the community prioritization. CR ank 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, CR ank can incorporate domain-specific information to further boost performance. Experiments on many large networks show that CR ank effectively prioritizes communities, yielding a nearly 50-fold improvement in community prioritization.