Сравнение методов

	Временная собственная центральность	Temporal PageRank
Область	Сетевой анализ	Сетевой анализ
Семейство	Machine learning	Machine learning
Год появления	2011-2017	2016
Автор метода	Grindrod, P.; Higham, D. J.; Taylor, D. et al.	Rozenshtein, P. & Gionis, A.
Тип	Centrality measure for temporal networks	Centrality / ranking algorithm for temporal networks
Основополагающий источник	Grindrod, P., Parsons, M. C., Higham, D. J., & Estrada, E. (2011). Communicability across evolving networks. Physical Review E, 83(4), 046120.	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	dynamic eigenvector centrality, time-varying eigenvector centrality, TEC, temporal communicability centrality	TPR, time-aware PageRank, streaming PageRank, dynamic PageRank
Связанные	5	6
Сводка	Temporal eigenvector centrality extends the classical eigenvector centrality to networks that change over time. By accounting for the ordering and timing of connections, it identifies nodes that are influential not merely because of many simultaneous connections, but because they sit at the crossroads of sequentially important pathways across multiple time slices of the network.	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.