So sánh phương pháp

	Phân tích khuếch tán mạng Bayes	Phân tích khuếch tán mạng theo thời gian
Lĩnh vực	Phân tích mạng lưới	Phân tích mạng lưới
Họ	Machine learning	Machine learning
Năm ra đời	2010s	2012
Người khởi xướng	Gomez Rodriguez, M.; Leskovec, J.; and related network science community	Holme, P. & Saramäki, J.
Loại	Probabilistic inference on network spreading processes	Network analysis framework
Công trình gốc	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.
Tên gọi khác	Bayesian diffusion model, probabilistic network diffusion, Bayesian spreading process inference, BNDA	TNDA, dynamic network diffusion, time-varying network spreading, diffusion on temporal networks
Liên quan	5	5
Tóm tắt	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.