Сравнение методов

	Временной анализ двухмодовых сетей	Выявление временных сообществ
Область	Сетевой анализ	Сетевой анализ
Семейство	Machine learning	Machine learning
Год появления	1990s–2010s	2010
Автор метода	Borgatti, S. P. & Everett, M. G. (two-mode foundations); extended to temporal setting by multiple authors	Mucha, P. J. et al.
Тип	Network analysis technique	Network clustering algorithm
Основополагающий источник	Borgatti, S. P., & Everett, M. G. (1997). Network analysis of 2-mode data. Social Networks, 19(3), 243–269.	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	temporal bipartite network analysis, dynamic two-mode network analysis, time-varying bipartite network analysis, longitudinal affiliation network analysis	dynamic community detection, time-varying community detection, evolutionary community detection, longitudinal community detection
Связанные	5	6
Сводка	Temporal two-mode network analysis tracks relationships between two distinct classes of nodes — such as authors and publications, or actors and events — across multiple time points. By combining bipartite structure with longitudinal observation, it reveals how affiliation patterns, collaborations, and community memberships form, evolve, and dissolve over time.	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.