Salīdzināt metodes

	Dinamiskā kopienu noteikšana	Modulāritātes analīze
Nozare	Tīklu analīze	Tīklu analīze
Saime	Machine learning	Machine learning
Izcelsmes gads	2010 (key formalization); earlier work 2002–2009	2004
Autors	Mucha, P. J. et al. (key formalization); earlier work by Girvan & Newman (2002)	Newman, M. E. J. & Girvan, M.
Tips	Graph clustering / community discovery	Community detection / graph partitioning
Pirmavots	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.	Newman, M. E. J., & Girvan, M. (2004). Finding and evaluating community structure in networks. Physical Review E, 69(2), 026113.
Citi nosaukumi	DCD, temporal community detection, evolving community detection, dynamic graph clustering	Q-modularity, community structure detection, network modularity optimization, graph partitioning by modularity
Saistītās	5	5
Kopsavilkums	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.	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.