手法を比較

	時間的近接性中心性	Temporal PageRank
分野	ネットワーク分析	ネットワーク分析
系統	Machine learning	Machine learning
提唱年	2011	2016
提唱者	Pan, R. K. & Saramaki, J.	Rozenshtein, P. & Gionis, A.
種類	Centrality measure (temporal)	Centrality / ranking algorithm for temporal networks
原典	Pan, R. K., & Saramaki, J. (2011). Path lengths, correlations, and centrality in temporal networks. Physical Review E, 84(1), 016105.	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.
別名	time-varying closeness centrality, dynamic closeness centrality, TCC, temporal reachability-based centrality	TPR, time-aware PageRank, streaming PageRank, dynamic PageRank
関連	6	6
概要	Temporal closeness centrality extends the classical closeness measure to time-varying networks by replacing static shortest paths with time-respecting (foremost) paths. It quantifies how quickly a node can reach all other nodes when interactions occur at specific moments in time, giving a more realistic picture of information flow, disease spread, and influence in dynamic systems.	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.