Linganisha mbinu

	Uchanganuzi wa Jumuiya unaotokana na nadharia ya Bayes	Uchambuzi wa Mitandao ya Kijamii
Nyanja	Uchanganuzi wa Mitandao	Uchanganuzi wa Mitandao
Familia	Machine learning	Machine learning
Mwaka wa asili	2001–2014	1934 (sociometry); 1994 (modern formalization)
Mwanzilishi	Nowicki, K. & Snijders, T. A. B. (formal Bayesian framing); extended by Peixoto, T. P.	Moreno, J.L.; formalized by Wasserman & Faust
Aina	Probabilistic generative model / inference	Structural/relational analysis framework
Chanzo asilia	Peixoto, T. P. (2014). Efficient Monte Carlo and greedy heuristic for the inference of stochastic block models. Physical Review E, 89(1), 012804.	Wasserman, S. & Faust, K. (1994). Social Network Analysis: Methods and Applications. Cambridge University Press. ISBN: 978-0-521-38707-1
Majina mbadala	Bayesian graph clustering, probabilistic community detection, Bayesian stochastic block model community detection, Bayesian network partitioning	SNA, network analysis, sociometric analysis, relational analysis
Zinazohusiana	5	5
Muhtasari	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.	Social Network Analysis (SNA) is a structural method that maps and measures relationships and flows between people, groups, organizations, or other entities modeled as nodes connected by ties (edges). Rather than focusing on individual attributes, SNA reveals how the pattern of connections shapes behavior, influence, information flow, and outcomes within a system.