Usporedite metode

	Temporal PageRank	Vremenska detekcija zajednica
Područje	Analiza mreža	Analiza mreža
Obitelj	Machine learning	Machine learning
Godina nastanka	2016	2010
Tvorac	Rozenshtein, P. & Gionis, A.	Mucha, P. J. et al.
Vrsta	Centrality / ranking algorithm for temporal networks	Network clustering algorithm
Temeljni izvor	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.
Drugi nazivi	TPR, time-aware PageRank, streaming PageRank, dynamic PageRank	dynamic community detection, time-varying community detection, evolutionary community detection, longitudinal community detection
Srodne	6	6
Sažetak	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.