Сравнение методов

	Байесовский анализ двухрежимных сетей	Многослойный двухрежимный сетевой анализ
Область	Сетевой анализ	Сетевой анализ
Семейство	Machine learning	Machine learning
Год появления	1997–2010s	2010s (synthesis of two-mode and multilayer frameworks)
Автор метода	Borgatti & Everett (two-mode SNA); Bayesian extensions by multiple authors	Kivela et al. (multilayer); Borgatti & Everett (two-mode foundations)
Тип	Probabilistic network model	Network analysis framework
Основополагающий источник	Borgatti, S. P., & Everett, M. G. (1997). Network analysis of 2-mode data. Social Networks, 19(3), 243–269.	Kivela, M., Arenas, A., Barthelemy, M., Gleeson, J. P., Moreno, Y., & Porter, M. A. (2014). Multilayer networks. Journal of Complex Networks, 2(3), 203–271.
Другие названия	Bayesian bipartite network analysis, probabilistic two-mode network analysis, Bayesian affiliation network analysis, Bayesian two-mode SNA	multilayer bipartite network analysis, multi-layer two-mode network, multiplex bipartite network analysis, ML-TMNA
Связанные	5	6
Сводка	Bayesian two-mode network analysis applies probabilistic Bayesian inference to bipartite (two-mode) networks — graphs linking two distinct sets of nodes such as actors and events, authors and papers, or consumers and products. By placing priors over tie probabilities and structural parameters, analysts obtain uncertainty estimates around centrality, community membership, and projection metrics rather than single-point estimates.	Multilayer two-mode network analysis extends bipartite (two-mode) network analysis to settings where actors and artifacts — people and publications, firms and markets, genes and diseases — are connected across multiple distinct relationship layers or time slices simultaneously. It captures how dual-membership structures evolve, overlap, or interact across contexts that a single-layer bipartite graph cannot represent.