Сравнение методов

	Анализ временной модулярности	Динамическое обнаружение сообществ
Область	Сетевой анализ	Сетевой анализ
Семейство	Machine learning	Machine learning
Год появления	2010	2010 (key formalization); earlier work 2002–2009
Автор метода	Mucha, P. J., Richardson, T., Macon, K., Porter, M. A., & Onnela, J.-P.	Mucha, P. J. et al. (key formalization); earlier work by Girvan & Newman (2002)
Тип	Community detection (temporal extension of modularity optimization)	Graph clustering / community discovery
Основополагающий источник	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.
Другие названия	dynamic modularity, time-varying modularity, longitudinal community detection, temporal community structure analysis	DCD, temporal community detection, evolving community detection, dynamic graph clustering
Связанные	5	5
Сводка	Temporal modularity analysis extends standard modularity-based community detection to time-varying networks by treating each time slice as a network layer and coupling adjacent layers with inter-temporal links. This allows researchers to identify how communities form, persist, merge, split, and dissolve over time in dynamic relational data.	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.