Salīdzināt metodes

	Analīze virzītai tīklu difūzijai	Daudzslāņu tīklu analīze
Nozare	Tīklu analīze	Tīklu analīze
Saime	Machine learning	Machine learning
Izcelsmes gads	2003 (influence maximization formalization); epidemic models traced to Kermack & McKendrick, 1927	2014
Autors	Kempe, D.; Kleinberg, J.; Tardos, E. (influence maximization); Pastor-Satorras, R. et al. (epidemic spreading)	Kivela, M.; Boccaletti, S. et al.
Tips	Network spreading and cascade analysis	Structural network model
Pirmavots	Kempe, D., Kleinberg, J., & Tardos, E. (2003). Maximizing the spread of influence through a social network. Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 137–146.	Kivela, M., Arenas, A., Barthelemy, M., Gleeson, J. P., Moreno, Y., & Porter, M. A. (2014). Multilayer networks. Journal of Complex Networks, 2(3), 203–271.
Citi nosaukumi	directed diffusion model, information spreading on directed networks, directed cascade analysis, directed influence propagation	multiplex networks, multi-layer network analysis, multilayer network analysis, MNA
Saistītās	6	6
Kopsavilkums	Directed network diffusion analysis studies how information, disease, behavior, or influence spreads through a network in which edges carry direction — meaning transmission flows one way along each link. It combines graph-theoretic representations with stochastic spreading models such as independent cascade, linear threshold, or SIR/SIS, and is central to influence maximization, epidemic forecasting, and information propagation research.	Multiplex network analysis studies systems where the same set of nodes is connected by multiple distinct types of relationships, each represented as a separate network layer. By analyzing layers simultaneously rather than in isolation, it reveals how different relation types interact, reinforce each other, or compensate for one another across the same actors or entities.