Compara mètodes

	PageRank Dinàmic	Centralitat de pas
Camp	Anàlisi de xarxes	Anàlisi de xarxes
Família	Machine learning	Machine learning
Any d'origen	2007–2016	1977
Autor original	Rozenshtein, P. & Gionis, A. (formalized); Page, L. & Brin, S. for base PageRank	Freeman, L. C.
Tipus	Centrality / ranking algorithm	Centrality measure
Font seminal	Rozenshtein, P., & Gionis, A. (2016). Temporal PageRank. In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), Lecture Notes in Computer Science, 9853, 674–689. Springer.	Freeman, L. C. (1977). A set of measures of centrality based on betweenness. Sociometry, 40(1), 35–41.
Àlies	Temporal PageRank, time-aware PageRank, evolving PageRank, DPR	Freeman betweenness, BC, geodesic betweenness, shortest-path betweenness
Relacionats	6	6
Resum	Dynamic PageRank extends the classic PageRank algorithm to networks whose edges carry timestamps, assigning importance scores that evolve over time. By discounting older links and emphasising recent connections, it identifies nodes that are influential at specific moments rather than across the entire network history, making it well-suited for web archives, citation streams, social media cascades, and any domain where link recency matters.	Betweenness centrality, formalized by Linton C. Freeman in 1977, measures how often a node lies on the shortest path connecting every other pair of nodes in a network. High-betweenness nodes act as bridges or brokers: removing them fragments the network into disconnected components more severely than removing any other nodes.