Salīdzināt metodes

	Dinamiskais PageRank	Temporālā kopienu noteikšana
Nozare	Tīklu analīze	Tīklu analīze
Saime	Machine learning	Machine learning
Izcelsmes gads	2007–2016	2010
Autors	Rozenshtein, P. & Gionis, A. (formalized); Page, L. & Brin, S. for base PageRank	Mucha, P. J. et al.
Tips	Centrality / ranking algorithm	Network clustering algorithm
Pirmavots	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.	Mucha, P. J., Richardson, T., Macon, K., Porter, M. A., & Onnela, J.-P. (2010). Community structure in time-dependent, multiscale, and multiplex networks. Science, 328(5980), 876–878.
Citi nosaukumi	Temporal PageRank, time-aware PageRank, evolving PageRank, DPR	dynamic community detection, time-varying community detection, evolutionary community detection, longitudinal community detection
Saistītās	6	6
Kopsavilkums	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.	Temporal community detection identifies cohesive groups (communities) in networks whose structure changes over time. By treating each time snapshot as a network layer and coupling consecutive layers, it reveals how communities form, merge, split, grow, or dissolve — turning a sequence of static snapshots into a continuous narrative of group evolution.