Salīdzināt metodes

	Temporālais PageRank	Laika starpības centrālās vērtības noteikšana
Nozare	Tīklu analīze	Tīklu analīze
Saime	Machine learning	Machine learning
Izcelsmes gads	2016	2012
Autors	Rozenshtein, P. & Gionis, A.	Kim, H. & Anderson, R.; Holme, P. & Saramäki, J.
Tips	Centrality / ranking algorithm for temporal networks	Centrality measure for temporal networks
Pirmavots	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), Part II, LNCS 9852, pp. 674–689. Springer.	Holme, P., & Saramäki, J. (2012). Temporal networks. Physics Reports, 519(3), 97–125.
Citi nosaukumi	TPR, time-aware PageRank, streaming PageRank, dynamic PageRank	TBC, time-varying betweenness centrality, dynamic betweenness centrality, time-respecting betweenness
Saistītās	6	6
Kopsavilkums	Temporal PageRank extends the classic PageRank algorithm to time-evolving networks by incorporating the recency and ordering of interactions. Edges are weighted by a decay function so that recent contacts contribute more to a node's score than old ones. The result is a dynamic importance ranking that captures who is influential right now, rather than over the entire history of the network.	Temporal Betweenness Centrality (TBC) extends classical betweenness centrality to time-stamped networks by counting how often a node lies on time-respecting shortest paths — paths that traverse edges in chronological order. It identifies nodes that act as temporal brokers, controlling information or resource flow as it evolves over time, rather than in a static snapshot.