Machine learningNetwork science

Dynamic PageRank

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.

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Sources

  1. 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. DOI: 10.1007/978-3-319-46131-1_40
  2. Berberich, K., Vazirgiannis, M., & Weikum, G. (2007). Time-aware authority ranking. Internet Mathematics, 3(4), 407–429. DOI: 10.1080/15427951.2006.10129134

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Referenced by

ScholarGateDynamic PageRank (Dynamic PageRank (Temporal Extension of the PageRank Algorithm)). Retrieved 2026-06-04 from https://scholargate.app/en/network-analysis/dynamic-pagerank