Salīdzināt metodes

	Analīze virzītai tīklu difūzijai	Virziena PageRank
Nozare	Tīklu analīze	Tīklu analīze
Saime	Machine learning	Machine learning
Izcelsmes gads	2003 (influence maximization formalization); epidemic models traced to Kermack & McKendrick, 1927	1998
Autors	Kempe, D.; Kleinberg, J.; Tardos, E. (influence maximization); Pastor-Satorras, R. et al. (epidemic spreading)	Brin, S. & Page, L.
Tips	Network spreading and cascade analysis	Iterative authority-scoring algorithm
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.	Brin, S. & Page, L. (1998). The anatomy of a large-scale hypertextual Web search engine. Proceedings of the 7th International Conference on World Wide Web (WWW7), 107–117. Elsevier.
Citi nosaukumi	directed diffusion model, information spreading on directed networks, directed cascade analysis, directed influence propagation	PageRank, PR, Google PageRank, directed link analysis
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.	Directed PageRank is a link-based authority scoring algorithm that assigns importance scores to nodes in a directed graph by iteratively redistributing rank through outgoing edges. Introduced by Brin and Page in 1998 as the backbone of Google Search, it measures not just how many in-links a node has but how authoritative the nodes pointing to it are.