手法を比較

	ベイジアンネットワーク拡散分析	時間的ネットワーク拡散分析
分野	ネットワーク分析	ネットワーク分析
系統	Machine learning	Machine learning
提唱年	2010s	2012
提唱者	Gomez Rodriguez, M.; Leskovec, J.; and related network science community	Holme, P. & Saramäki, J.
種類	Probabilistic inference on network spreading processes	Network analysis framework
原典	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.	Holme, P. & Saramäki, J. (2012). Temporal networks. Physics Reports, 519(3), 97–125.
別名	Bayesian diffusion model, probabilistic network diffusion, Bayesian spreading process inference, BNDA	TNDA, dynamic network diffusion, time-varying network spreading, diffusion on temporal networks
関連	5	5
概要	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.	Temporal Network Diffusion Analysis studies how information, disease, influence, or other contagions spread through networks whose structure changes over time. By modeling edges as time-stamped contacts rather than static links, it captures the critical role of timing and ordering in determining which nodes get reached, how fast, and through which pathways — producing conclusions that static network models systematically miss.