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	गत्यात्मक समुदाय संसूचन	टेम्पोरल कम्युनिटी डिटेक्शन (Temporal Community Detection)
क्षेत्र	नेटवर्क विश्लेषण	नेटवर्क विश्लेषण
परिवार	Machine learning	Machine learning
उद्भव वर्ष	2010 (key formalization); earlier work 2002–2009	2010
प्रवर्तक	Mucha, P. J. et al. (key formalization); earlier work by Girvan & Newman (2002)	Mucha, P. J. et al.
प्रकार	Graph clustering / community discovery	Network clustering algorithm
मौलिक स्रोत	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.	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.
उपनाम	DCD, temporal community detection, evolving community detection, dynamic graph clustering	dynamic community detection, time-varying community detection, evolutionary community detection, longitudinal community detection
संबंधित	5	6
सारांश	Dynamic community detection identifies groups of densely connected nodes in networks that evolve over time, tracking how communities form, merge, split, and dissolve across temporal snapshots. Developed to extend static modularity optimization to time-varying structures, it is widely used in social, biological, and communication network research.	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.