방법 비교

	Bayesian PageRank	베이지안 네트워크 확산 분석
분야	네트워크 분석	네트워크 분석
계열	Machine learning	Machine learning
기원 연도	1999 (PageRank); 2000s (Bayesian extension)	2010s
창시자	Page, L. & Brin, S. (PageRank); Bayesian extension by multiple authors	Gomez Rodriguez, M.; Leskovec, J.; and related network science community
유형	Probabilistic centrality measure	Probabilistic inference on network spreading processes
원전	Page, L., Brin, S., Motwani, R., & Winograd, T. (1999). The PageRank citation ranking: Bringing order to the web. Stanford InfoLab Technical Report.	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.
별칭	Bayesian PR, probabilistic PageRank, uncertainty-aware PageRank, stochastic PageRank	Bayesian diffusion model, probabilistic network diffusion, Bayesian spreading process inference, BNDA
관련	6	5
요약	Bayesian PageRank extends the classic PageRank algorithm by embedding it within a Bayesian probabilistic framework. Instead of returning a single deterministic rank score for each node, it quantifies uncertainty over rank estimates — particularly valuable when the network is incomplete, noisy, or observed with error. It is used in web analysis, citation networks, and social network research where rank uncertainty matters.	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.