Deterministic graph-theoretic algorithm for detecting modules in biological interaction networks

Roger L. Chang, Feng Luo, Stuart Johnson, Richard H. Scheuermann

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


An approach for module identification, Modules of Networks (MoNet), introduced an intuitive module definition and clear detection method using edges ranked by the Girvan-Newman algorithm. Modules from a yeast network showed significant association with biological processes, indicating the method’s utility; however, systematic bias leads to varied results across trials. MoNet modules also exclude some network regions. To address these shortcomings, we developed a deterministic version of the Girvan-Newman algorithm and a new agglomerative algorithm, Deterministic Modularization of Networks (dMoNet). dMoNet simultaneously processes structurally equivalent edges while preserving intuitive foundations of the MoNet algorithm and generates modules with full network coverage.

Original languageEnglish (US)
Pages (from-to)101-119
Number of pages19
JournalInternational Journal of Bioinformatics Research and Applications
Issue number2
StatePublished - 2010
Externally publishedYes


  • algorithms
  • betweenness
  • bioinformatics
  • deterministic modularization of networks
  • dMoNet
  • gene ontology
  • Girvan-Newman
  • GO
  • graph theory
  • interaction networks
  • modules

ASJC Scopus subject areas

  • Biomedical Engineering
  • Health Informatics
  • Clinical Biochemistry
  • Health Information Management


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