방법 비교

	동적 PageRank	고유벡터 중심성
분야	네트워크 분석	네트워크 분석
계열	Machine learning	Machine learning
기원 연도	2007–2016	1972
창시자	Rozenshtein, P. & Gionis, A. (formalized); Page, L. & Brin, S. for base PageRank	Bonacich, P.
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.	Bonacich, P. (1972). Factoring and weighting approaches to status scores and clique identification. Journal of Mathematical Sociology, 2(1), 113–120.
별칭	Temporal PageRank, time-aware PageRank, evolving PageRank, DPR	eigenvector centrality, EC, Bonacich centrality, power centrality
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.	Eigenvector centrality, introduced by Bonacich in 1972, measures a node's influence by considering not just how many neighbors it has, but how influential those neighbors are. A node scores highly if it is connected to other high-scoring nodes, making it a recursive, globally-aware measure of structural importance in a network.