Methoden vergleichen

	Dynamische Eigenvektor-Zentralität	Dynamisches PageRank
Fachgebiet	Netzwerkanalyse	Netzwerkanalyse
Familie	Machine learning	Machine learning
Entstehungsjahr	2010s	2007–2016
Urheber	Lerman, K.; Ghosh, R.; Kang, J. H.	Rozenshtein, P. & Gionis, A. (formalized); Page, L. & Brin, S. for base PageRank
Typ	Centrality measure for time-evolving networks	Centrality / ranking algorithm
Wegweisende Quelle	Lerman, K., Ghosh, R., & Kang, J. H. (2010). Centrality metric for dynamic networks. Proceedings of the 8th Workshop on Mining and Learning with Graphs (MLG '10). ACM.	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.
Aliasnamen	temporal eigenvector centrality, time-varying eigenvector centrality, dynamic EC, evolving eigenvector centrality	Temporal PageRank, time-aware PageRank, evolving PageRank, DPR
Verwandt	4	6
Zusammenfassung	Dynamic eigenvector centrality extends the classic eigenvector centrality measure to networks that change over time. Rather than computing a single leading eigenvector on a static adjacency matrix, it tracks how a node's influence — defined by the importance of its neighbours — evolves across snapshots or time windows. The method is used in social network analysis, epidemiology, and information diffusion studies where network topology shifts continuously.	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.