방법 비교

	베이지안 사회 연결망 분석	네트워크 확산 분석
분야	네트워크 분석	네트워크 분석
계열	Machine learning	Machine learning
기원 연도	2002	1927 (epidemic roots); network formalization 1990s–2000s
창시자	Hoff, P. D.; Raftery, A. E.; Handcock, M. S.	Kermack, W. O. & McKendrick, A. G.
유형	Probabilistic / Bayesian network model	Simulation / analytical model
원전	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.	Kermack, W. O. & McKendrick, A. G. (1927). A contribution to the mathematical theory of epidemics. Proceedings of the Royal Society of London A, 115(772), 700–721.
별칭	Bayesian SNA, Bayesian network modeling, probabilistic social network analysis, Bayesian relational modeling	diffusion on networks, information diffusion, contagion spreading model, network propagation model
관련	5	5
요약	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.	Network diffusion analysis models how information, diseases, behaviors, or innovations spread across a graph of nodes and edges. Drawing on classical epidemic theory (SI, SIR, SIS) and modern network science, it tracks which nodes become infected, how quickly, and whether the spread reaches a global cascade or dies out locally.