  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.
