방법 비교

	동적 PageRank	Betweenness Centrality
분야	네트워크 분석	네트워크 분석
계열	Machine learning	Machine learning
기원 연도	2007–2016	1977
창시자	Rozenshtein, P. & Gionis, A. (formalized); Page, L. & Brin, S. for base PageRank	Freeman, L. C.
유형	Centrality / ranking algorithm	Centrality measure
원전	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.
별칭	Temporal PageRank, time-aware PageRank, evolving PageRank, DPR	Freeman betweenness, BC, geodesic betweenness, shortest-path betweenness
관련	6	6
요약	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.