Machine learningNetwork science
Temporal Community Detection
Temporal community detection identifies cohesive groups (communities) in networks whose structure changes over time. By treating each time snapshot as a network layer and coupling consecutive layers, it reveals how communities form, merge, split, grow, or dissolve — turning a sequence of static snapshots into a continuous narrative of group evolution.
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Sources
- 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 ↗
- Rossetti, G., & Cazabet, R. (2018). Community discovery in dynamic networks: A survey. ACM Computing Surveys, 51(2), 1–37. DOI: 10.1145/3172867 ↗
Related methods
Referenced by
Bayesian Community DetectionDynamic Closeness CentralityDynamic Community DetectionDynamic Eigenvector CentralityDynamic Modularity AnalysisDynamic PageRankDynamic Two-Mode Network AnalysisMultilayer Temporal Network AnalysisTemporal Knowledge Graph AnalysisTemporal Modularity AnalysisTemporal Multiplex Network AnalysisTemporal Network Diffusion AnalysisTemporal PageRankTemporal Social Network AnalysisTemporal Stochastic Block ModelTemporal Two-Mode Network Analysis