Salīdzināt metodes

	Analīze virzītai tīklu difūzijai	Tīkla difūzijas analīze
Nozare	Tīklu analīze	Tīklu analīze
Saime	Machine learning	Machine learning
Izcelsmes gads	2003 (influence maximization formalization); epidemic models traced to Kermack & McKendrick, 1927	1927 (epidemic roots); network formalization 1990s–2000s
Autors	Kempe, D.; Kleinberg, J.; Tardos, E. (influence maximization); Pastor-Satorras, R. et al. (epidemic spreading)	Kermack, W. O. & McKendrick, A. G.
Tips	Network spreading and cascade analysis	Simulation / analytical 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.	Kermack, W. O. & McKendrick, A. G. (1927). A contribution to the mathematical theory of epidemics. Proceedings of the Royal Society of London A, 115(772), 700–721.
Citi nosaukumi	directed diffusion model, information spreading on directed networks, directed cascade analysis, directed influence propagation	diffusion on networks, information diffusion, contagion spreading model, network propagation model
Saistītās	6	5
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.	Network diffusion analysis models how information, diseases, behaviors, or innovations spread across a graph of nodes and edges. Drawing on classical epidemic theory (SI, SIR, SIS) and modern network science, it tracks which nodes become infected, how quickly, and whether the spread reaches a global cascade or dies out locally.