Uncovering modular structure in networks is fundamental for advancing the understanding of complex systems in biology, physics, engineering, and technology . Community detection provides a way to computationally identify candidate modules as hypotheses, which then need to be experimentally validated . However, validation of detected communities requires expensive and time consuming experimental methods, such as mutagenesis in a wet biological laboratory. As a consequence only a limited number of communities can be experimentally validated, and it is thus important to determine which communities to select for downstream validation and experimentation. Here we develop CRank, an automatic method for prioritizing network communities and identifying the most promising ones for further experimentation . CRank efficiently evaluates robustness and magnitude of structural features of each community and then combines these features to obtain the community prioritization. CRank can be used with any community detection method and scales to large networks . 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 diverse and important biological networks demonstrate that the proposed approach effectively prioritizes communities, yielding a nearly 50-fold improvement in community prioritization over a baseline ordering of detected communities. Taken together, CRank represents a network-based approach to identify high-quality communities, even for domains at the frontier of science where supervised meta information is not available .
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 .
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advancing the understanding of complex
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engineering, and technology
and engineering
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provides a way to computationally identify
identifies
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experimentally validated . However, validation of detected communities requires expensive and time consuming experimental methods,
validated through experiments,
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wet
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As a consequence only a limited number of communities can be experimentally
Only a few communities can typically be
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determine
prioritize
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validation and
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an automatic method
a mathematically principled approach
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and identifying the most promising ones for further experimentation
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to obtain
into
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and scales to large networks
R
diverse and important biological networks demonstrate that the proposed approach
large networks show that CRank
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over a baseline ordering of detected communities. Taken together, CRank represents a network-based approach to identify high-quality communities, even for domains at the frontier of science where supervised meta information is not available