Machine learningNetwork science

Dynamic Modularity Analysis

Dynamic modularity analysis extends the classical modularity framework to networks that evolve over time, detecting communities across a sequence of network snapshots while penalizing unnecessary community changes between time steps. It identifies cohesive groups and tracks how they form, merge, split, or dissolve, giving researchers a principled view of structural change in longitudinal network data.

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Sources

  1. Mucha, P. J., Richardson, T., Macon, K., Porter, M. A., & Onnela, J.-P. (2010). Community structure in time-dependent, multiscale, and multiplex networks. Science, 328(5980), 876–878. DOI: 10.1126/science.1184819
  2. Blondel, V. D., Guillaume, J.-L., Lambiotte, R., & Lefebvre, E. (2008). Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, 2008(10), P10008. DOI: 10.1088/1742-5468/2008/10/P10008

Related methods

ScholarGateDynamic Modularity Analysis (Dynamic Modularity Analysis (Temporal Community Structure Detection)). Retrieved 2026-06-04 from https://scholargate.app/tr/network-analysis/dynamic-modularity-analysis