قارن الطرق

	تحليل الشبكات ثنائية الوضع البايزي	الكشف عن المجتمعات البايزية
المجال	تحليل الشبكات	تحليل الشبكات
العائلة	Machine learning	Machine learning
سنة النشأة	1997–2010s	2001–2014
صاحب الطريقة	Borgatti & Everett (two-mode SNA); Bayesian extensions by multiple authors	Nowicki, K. & Snijders, T. A. B. (formal Bayesian framing); extended by Peixoto, T. P.
النوع	Probabilistic network model	Probabilistic generative model / inference
المصدر التأسيسي	Borgatti, S. P., & Everett, M. G. (1997). Network analysis of 2-mode data. Social Networks, 19(3), 243–269.	Peixoto, T. P. (2014). Efficient Monte Carlo and greedy heuristic for the inference of stochastic block models. Physical Review E, 89(1), 012804.
الأسماء البديلة	Bayesian bipartite network analysis, probabilistic two-mode network analysis, Bayesian affiliation network analysis, Bayesian two-mode SNA	Bayesian graph clustering, probabilistic community detection, Bayesian stochastic block model community detection, Bayesian network partitioning
ذات صلة	5	5
الملخص	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 community detection infers latent group structure in networks by treating community membership as unobserved variables and using Bayesian inference — typically via Markov chain Monte Carlo or variational methods — to compute a posterior distribution over all plausible partitions. Unlike modularity optimisation, it selects the number of communities from data and provides principled uncertainty estimates for every node assignment.