Bandingkan metode

	Dynamic PageRank	Sentralitas Derajat
Bidang	Analisis Jaringan	Analisis Jaringan
Keluarga	Machine learning	Machine learning
Tahun asal	2007–2016	1978
Pencetus	Rozenshtein, P. & Gionis, A. (formalized); Page, L. & Brin, S. for base PageRank	Freeman, L. C.
Tipe	Centrality / ranking algorithm	Node-level centrality measure
Sumber perintis	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. (1978). Centrality in social networks: Conceptual clarification. Social Networks, 1(3), 215–239.
Alias	Temporal PageRank, time-aware PageRank, evolving PageRank, DPR	node degree, degree score, DC, connectivity centrality
Terkait	6	6
Ringkasan	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.	Degree centrality is the simplest and most intuitive measure of a node's importance in a network, defined as the number of direct ties a node has to other nodes. Normalized by dividing by the maximum possible ties, it allows comparison across networks of different sizes and is the starting point of almost every network analysis.