Linganisha mbinu

	Uchanganuzi wa Mtandao wa Aina Mbili wa Kibayesiani	Uchambuzi wa Mitandao Tabaka-Nyingi Njia-Mbili
Nyanja	Uchanganuzi wa Mitandao	Uchanganuzi wa Mitandao
Familia	Machine learning	Machine learning
Mwaka wa asili	1997–2010s	2010s (synthesis of two-mode and multilayer frameworks)
Mwanzilishi	Borgatti & Everett (two-mode SNA); Bayesian extensions by multiple authors	Kivela et al. (multilayer); Borgatti & Everett (two-mode foundations)
Aina	Probabilistic network model	Network analysis framework
Chanzo asilia	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.
Majina mbadala	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
Zinazohusiana	5	6
Muhtasari	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.