השוואת שיטות

	ניתוח רשתות דו-מצבי בייסיאני	ניתוח רשתות דו-מודאליות משוקללות
תחום	ניתוח רשתות	ניתוח רשתות
משפחה	Machine learning	Machine learning
שנת המקור	1997–2010s	1997 (two-mode); weighted extensions 2000s
הוגה השיטה	Borgatti & Everett (two-mode SNA); Bayesian extensions by multiple authors	Borgatti, S. P. & Everett, M. G.
סוג	Probabilistic network model	Network structural analysis
מקור מכונן	Borgatti, S. P., & Everett, M. G. (1997). Network analysis of 2-mode data. Social Networks, 19(3), 243–269.	Borgatti, S. P., & Everett, M. G. (1997). Network analysis of 2-mode data. Social Networks, 19(3), 243–269.
כינויים	Bayesian bipartite network analysis, probabilistic two-mode network analysis, Bayesian affiliation network analysis, Bayesian two-mode SNA	weighted bipartite network analysis, valued two-mode network analysis, weighted affiliation network analysis, W2MNA
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.	Weighted two-mode network analysis examines bipartite graphs in which two distinct node sets — such as actors and events, authors and papers, or species and habitats — are connected by edges carrying numerical weights that capture the strength, frequency, or intensity of each affiliation. Incorporating weights provides substantially richer structural insights than unweighted bipartite analysis.