قارن الطرق

	PageRank الزمني	تحليل الشبكات الاجتماعية الزمنية
المجال	تحليل الشبكات	تحليل الشبكات
العائلة	Machine learning	Machine learning
سنة النشأة	2016	2000s–2010s
صاحب الطريقة	Rozenshtein, P. & Gionis, A.	Moody, J.; Holme, P.; Saramäki, J.
النوع	Centrality / ranking algorithm for temporal networks	Longitudinal network analysis
المصدر التأسيسي	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.
الأسماء البديلة	TPR, time-aware PageRank, streaming PageRank, dynamic PageRank	TSNA, longitudinal social network analysis, time-varying network analysis, dynamic SNA
ذات صلة	6	4
الملخص	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 Social Network Analysis (TSNA) extends classic social network analysis by treating networks as time-varying structures. Rather than aggregating all ties into a single static snapshot, TSNA tracks when ties form, persist, and dissolve, enabling researchers to study how social structures evolve and how dynamic connectivity shapes diffusion, influence, and inequality over time.