방법 비교

	베이즈 이중 모드 네트워크 분석	베이지안 사회 연결망 분석
분야	네트워크 분석	네트워크 분석
계열	Machine learning	Machine learning
기원 연도	1997–2010s	2002
창시자	Borgatti & Everett (two-mode SNA); Bayesian extensions by multiple authors	Hoff, P. D.; Raftery, A. E.; Handcock, M. S.
유형	Probabilistic network model	Probabilistic / Bayesian network model
원전	Borgatti, S. P., & Everett, M. G. (1997). Network analysis of 2-mode data. Social Networks, 19(3), 243–269.	Hoff, P. D., Raftery, A. E., & Handcock, M. S. (2002). Latent space approaches to social network analysis. Journal of the American Statistical Association, 97(460), 1090–1098.
별칭	Bayesian bipartite network analysis, probabilistic two-mode network analysis, Bayesian affiliation network analysis, Bayesian two-mode SNA	Bayesian SNA, Bayesian network modeling, probabilistic social network analysis, Bayesian relational modeling
관련	5	5
요약	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.	Bayesian Social Network Analysis applies Bayesian probabilistic inference to relational data, placing prior distributions over network parameters and updating them with observed tie data to yield full posterior distributions over structural features, tie probabilities, and latent actor positions. It enables principled uncertainty quantification in network models, making it especially valuable when data are sparse, partially observed, or subject to measurement error.