Сравнение методов

	Байесовский анализ эгосетей	Байесовский анализ социальных сетей
Область	Сетевой анализ	Сетевой анализ
Семейство	Machine learning	Machine learning
Год появления	2010s	2002
Автор метода	Various (Bayesian SNA tradition; Krivitsky, Kolaczyk, Handcock among key contributors)	Hoff, P. D.; Raftery, A. E.; Handcock, M. S.
Тип	Probabilistic network model	Probabilistic / Bayesian network model
Основополагающий источник	Krivitsky, P. N., & Kolaczyk, E. D. (2015). On the question of effective sample size in network modeling: An asymptotic inquiry. Statistical Science, 30(2), 184–198.	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.
Другие названия	Bayesian personal network analysis, Bayesian egocentric network analysis, probabilistic ego network modeling, Bayesian egonet	Bayesian SNA, Bayesian network modeling, probabilistic social network analysis, Bayesian relational modeling
Связанные	5	5
Сводка	Bayesian ego network analysis applies probabilistic inference to ego-centered (personal) network data, combining a likelihood model for the ego's local network with prior distributions over network parameters. The result is a full posterior distribution that quantifies uncertainty about structural features such as alter composition, tie density, and network size — rather than producing point estimates alone.	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.