手法を比較

	Temporal PageRank	時間的コミュニティ検出
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	2016	2010
提唱者	Rozenshtein, P. & Gionis, A.	Mucha, P. J. et al.
種類	Centrality / ranking algorithm for temporal networks	Network clustering algorithm
原典	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.	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.
別名	TPR, time-aware PageRank, streaming PageRank, dynamic PageRank	dynamic community detection, time-varying community detection, evolutionary community detection, longitudinal community detection
関連	6	6
概要	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.	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.