Сравнение методов

	Динамический PageRank	Динамическое обнаружение сообществ
Область	Сетевой анализ	Сетевой анализ
Семейство	Machine learning	Machine learning
Год появления	2007–2016	2010 (key formalization); earlier work 2002–2009
Автор метода	Rozenshtein, P. & Gionis, A. (formalized); Page, L. & Brin, S. for base PageRank	Mucha, P. J. et al. (key formalization); earlier work by Girvan & Newman (2002)
Тип	Centrality / ranking algorithm	Graph clustering / community discovery
Основополагающий источник	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.
Другие названия	Temporal PageRank, time-aware PageRank, evolving PageRank, DPR	DCD, temporal community detection, evolving community detection, dynamic graph clustering
Связанные	6	5
Сводка	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.	Dynamic community detection identifies groups of densely connected nodes in networks that evolve over time, tracking how communities form, merge, split, and dissolve across temporal snapshots. Developed to extend static modularity optimization to time-varying structures, it is widely used in social, biological, and communication network research.