Process / pipeline
Community Detection — Graph Clustering in Networks
Community detection is a family of graph-partitioning algorithms that discover densely connected sub-groups — communities — within a network. First formalised through the modularity measure by Girvan and Newman (2002), the field advanced rapidly with the Louvain method (Blondel et al., 2008), the Leiden refinement (Traag et al., 2019), and the information-theoretic Infomap approach. All variants answer the same question: which nodes cluster together more tightly among themselves than with the rest of the network?
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Sources
- Blondel, V.D., Guillaume, J.-L., Lambiotte, R. & Lefebvre, E. (2008). Fast Unfolding of Communities in Large Networks. Journal of Statistical Mechanics, 2008(10), P10008. DOI: 10.1088/1742-5468/2008/10/P10008 ↗
- Traag, V.A., Waltman, L. & van Eck, N.J. (2019). From Louvain to Leiden: Guaranteeing Well-Connected Communities. Scientific Reports, 9, 5233. link ↗
Related methods
Referenced by
Bayesian Stochastic Block ModelBipartite Network AnalysisCentrality AnalysisDynamic Modularity AnalysisEgo Network AnalysisExponential Random Graph ModelGraph Neural Network (Network Analysis)k-Core DecompositionLandscape MetricsLink PredictionMultilayer Community DetectionMultilayer Network AnalysisMultilayer Social Network AnalysisMultiplex Network AnalysisNetwork Diffusion ModelsNetwork EmbeddingNetwork Motif AnalysisNetwork Resilience AnalysisProcess MiningSmall-World and Scale-Free Network AnalysisTemporal Network AnalysisTwo-mode Network AnalysisWeighted Community Detection