Сравнение методов

	Временная центральность по степени	Temporal PageRank
Область	Сетевой анализ	Сетевой анализ
Семейство	Machine learning	Machine learning
Год появления	2011–2012	2016
Автор метода	Holme, P.; Saramaki, J.; Kim, H.; Anderson, R.	Rozenshtein, P. & Gionis, A.
Тип	Centrality measure (temporal extension)	Centrality / ranking algorithm for temporal networks
Основополагающий источник	Holme, P. & Saramaki, J. (2012). Temporal networks. Physics Reports, 519(3), 97–125.	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), Part II, LNCS 9852, pp. 674–689. Springer.
Другие названия	time-varying degree centrality, dynamic degree centrality, temporal node degree, TDC	TPR, time-aware PageRank, streaming PageRank, dynamic PageRank
Связанные	6	6
Сводка	Temporal degree centrality extends the classic degree centrality to time-varying networks by counting how many distinct contacts a node accumulates over time. Rather than collapsing a dynamic network into a single static graph, it preserves the temporal order of edges, yielding a more faithful measure of a node's activity and reachability across the observation window.	Temporal PageRank extends the classic PageRank algorithm to time-evolving networks by incorporating the recency and ordering of interactions. Edges are weighted by a decay function so that recent contacts contribute more to a node's score than old ones. The result is a dynamic importance ranking that captures who is influential right now, rather than over the entire history of the network.