השוואת שיטות

	ניתוח רשתות דו-מצבי בייסיאני	ניתוח רשתות רב-שכבתיות דו-מצביות
תחום	ניתוח רשתות	ניתוח רשתות
משפחה	Machine learning	Machine learning
שנת המקור	1997–2010s	2010s (synthesis of two-mode and multilayer frameworks)
הוגה השיטה	Borgatti & Everett (two-mode SNA); Bayesian extensions by multiple authors	Kivela et al. (multilayer); Borgatti & Everett (two-mode foundations)
סוג	Probabilistic network model	Network analysis framework
מקור מכונן	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.
כינויים	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
קשורות	5	6
תקציר	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.