Compară metode

	PageRank dinamic	Analiza Rețelelor Temporale
Domeniu	Analiza rețelelor	Analiza rețelelor
Familie	Machine learning	Process / pipeline
Anul apariției	2007–2016	2012
Autorul original	Rozenshtein, P. & Gionis, A. (formalized); Page, L. & Brin, S. for base PageRank	Holme & Saramäki (2012) — seminal framework
Tip	Centrality / ranking algorithm	Dynamic graph analysis
Sursa seminală	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.	Holme, P. & Saramäki, J. (2012). Temporal Networks. Physics Reports, 519(3), 97-125.
Denumiri alternative	Temporal PageRank, time-aware PageRank, evolving PageRank, DPR	dynamic network analysis, time-varying network analysis, Zamansal Ağ Analizi (Temporal / Dynamic Networks)
Înrudite	6	3
Rezumat	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.	Temporal network analysis, formalised by Holme and Saramäki in their landmark 2012 Physics Reports survey, is the study of networks in which edges appear and disappear over time. Rather than collapsing all contacts into a single static graph, the approach preserves the precise timing of interactions — whether as contact sequences, time-stamped event lists, or windowed snapshots — and uses that timing to track how influence, disease, or information can actually propagate through the system.