方法对比

	贝叶斯网络扩散分析	贝叶斯随机块模型
领域	网络分析	网络分析
方法族	Machine learning	Machine learning
起源年份	2010s	2001–2014
提出者	Gomez Rodriguez, M.; Leskovec, J.; and related network science community	Nowicki, K. & Snijders, T. A. B.; extended by Peixoto, T. P.
类型	Probabilistic inference on network spreading processes	Probabilistic generative model with Bayesian inference
开创性文献	Gomez Rodriguez, M., Leskovec, J., & Scholkopf, B. (2012). Structure and Dynamics of Information Pathways in Online Media. Proceedings of the 6th ACM International Conference on Web Search and Data Mining (WSDM), 23–32.	Peixoto, T. P. (2014). Efficient Monte Carlo and greedy heuristic for the inference of stochastic block models. Physical Review E, 89(1), 012804.
别名	Bayesian diffusion model, probabilistic network diffusion, Bayesian spreading process inference, BNDA	Bayesian SBM, B-SBM, probabilistic block model, Bayesian community detection model
相关	5	5
摘要	Bayesian Network Diffusion Analysis applies Bayesian probabilistic inference to the study of how information, diseases, behaviors, or innovations propagate through a network. By placing priors over diffusion parameters and updating them with observed cascade data, it quantifies transmission rates, identifies influential spreaders, reconstructs latent propagation pathways, and provides full uncertainty estimates — all within a principled statistical framework.	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.