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Nat Commun. 2018 Jun 29;9(1):2544. doi: 10.1038/s41467-018-04948-5.

Prioritizing network communities.

Author information

1
Computer Science Department, Stanford University, 353 Serra Mall, Stanford, CA, 94305, USA.
2
Computer Science Department, Stanford University, 353 Serra Mall, Stanford, CA, 94305, USA. jure@cs.stanford.edu.
3
Chan Zuckerberg Biohub, 499 Illinois St., San Francisco, CA, 94158, USA. jure@cs.stanford.edu.

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.

PMID:
29959323
PMCID:
PMC6026212
DOI:
10.1038/s41467-018-04948-5
[Indexed for MEDLINE]
Free PMC Article

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