방법 비교

	베이지안 에고 네트워크 분석	베이즈 확률적 블록 모델
분야	네트워크 분석	네트워크 분석
계열	Machine learning	Machine learning
기원 연도	2010s	2001–2014
창시자	Various (Bayesian SNA tradition; Krivitsky, Kolaczyk, Handcock among key contributors)	Nowicki, K. & Snijders, T. A. B.; extended by Peixoto, T. P.
유형	Probabilistic network model	Probabilistic generative model with Bayesian inference
원전	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.	Peixoto, T. P. (2014). Efficient Monte Carlo and greedy heuristic for the inference of stochastic block models. Physical Review E, 89(1), 012804.
별칭	Bayesian personal network analysis, Bayesian egocentric network analysis, probabilistic ego network modeling, Bayesian egonet	Bayesian SBM, B-SBM, probabilistic block model, Bayesian community detection model
관련	5	5
요약	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.	The Bayesian Stochastic Block Model (Bayesian SBM) is a principled probabilistic method for community detection in networks. It treats group membership as a latent variable and uses Bayesian inference to simultaneously recover block structure and select the number of communities, avoiding the resolution-limit bias that plagues modularity-based approaches.