Σύγκριση μεθόδων

	Μπεϋζιανή Ανάλυση Κοινωνικών Δικτύων	Ανάλυση Κοινωνικών Δικτύων
Πεδίο	Ανάλυση Δικτύων	Ανάλυση Δικτύων
Οικογένεια	Machine learning	Machine learning
Έτος προέλευσης	2002	1934 (sociometry); 1994 (modern formalization)
Δημιουργός	Hoff, P. D.; Raftery, A. E.; Handcock, M. S.	Moreno, J.L.; formalized by Wasserman & Faust
Τύπος	Probabilistic / Bayesian network model	Structural/relational analysis framework
Θεμελιώδης πηγή	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.	Wasserman, S. & Faust, K. (1994). Social Network Analysis: Methods and Applications. Cambridge University Press. ISBN: 978-0-521-38707-1
Εναλλακτικές ονομασίες	Bayesian SNA, Bayesian network modeling, probabilistic social network analysis, Bayesian relational modeling	SNA, network analysis, sociometric analysis, relational analysis
Συναφείς	5	5
Σύνοψη	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.	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.