Salīdzināt metodes

	Beijesa tīkla difūzijas analīze	Sociālo tīklu analīze
Nozare	Tīklu analīze	Tīklu analīze
Saime	Machine learning	Machine learning
Izcelsmes gads	2010s	1934 (sociometry); 1994 (modern formalization)
Autors	Gomez Rodriguez, M.; Leskovec, J.; and related network science community	Moreno, J.L.; formalized by Wasserman & Faust
Tips	Probabilistic inference on network spreading processes	Structural/relational analysis framework
Pirmavots	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.	Wasserman, S. & Faust, K. (1994). Social Network Analysis: Methods and Applications. Cambridge University Press. ISBN: 978-0-521-38707-1
Citi nosaukumi	Bayesian diffusion model, probabilistic network diffusion, Bayesian spreading process inference, BNDA	SNA, network analysis, sociometric analysis, relational analysis
Saistītās	5	5
Kopsavilkums	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.	Social Network Analysis (SNA) is a structural method that maps and measures relationships and flows between people, groups, organizations, or other entities modeled as nodes connected by ties (edges). Rather than focusing on individual attributes, SNA reveals how the pattern of connections shapes behavior, influence, information flow, and outcomes within a system.