Machine learningNetwork science
Modularity Analysis
Modularity analysis is a network science method, formalized by Newman and Girvan in 2004, that detects community structure in graphs by measuring whether edges are more concentrated within groups than expected by chance. Its scalar quality index Q guides algorithms that partition nodes into cohesive clusters, making it the most widely adopted framework for community detection in social, biological, and technological networks.
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Sources
- Newman, M. E. J., & Girvan, M. (2004). Finding and evaluating community structure in networks. Physical Review E, 69(2), 026113. DOI: 10.1103/PhysRevE.69.026113 ↗
- Newman, M. E. J. (2006). Modularity and community structure in networks. Proceedings of the National Academy of Sciences, 103(23), 8577–8582. DOI: 10.1073/pnas.0601602103 ↗
Related methods
Referenced by
Bayesian Community DetectionBayesian Exponential Random Graph ModelBayesian Stochastic Block ModelBetweenness CentralityDegree CentralityDirected Community DetectionDirected Modularity AnalysisDynamic Community DetectionDynamic Modularity AnalysisDynamic Stochastic Block ModelEigenvector CentralityKnowledge Graph AnalysisMultilayer Community DetectionNetwork Diffusion AnalysisSocial Network AnalysisTemporal Community DetectionTemporal Modularity AnalysisTemporal Two-Mode Network AnalysisTwo-mode Network AnalysisWeighted Community DetectionWeighted Modularity AnalysisWeighted Social Network AnalysisWeighted Stochastic Block ModelWeighted Two-Mode Network Analysis