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| Temporales PageRank× | Temporale Zwischenzentralität× | |
|---|---|---|
| Fachgebiet | Netzwerkanalyse | Netzwerkanalyse |
| Familie | Machine learning | Machine learning |
| Entstehungsjahr≠ | 2016 | 2012 |
| Urheber≠ | Rozenshtein, P. & Gionis, A. | Kim, H. & Anderson, R.; Holme, P. & Saramäki, J. |
| Typ≠ | Centrality / ranking algorithm for temporal networks | Centrality measure for temporal networks |
| Wegweisende Quelle≠ | 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. DOI ↗ | Holme, P., & Saramäki, J. (2012). Temporal networks. Physics Reports, 519(3), 97–125. DOI ↗ |
| Aliasnamen | TPR, time-aware PageRank, streaming PageRank, dynamic PageRank | TBC, time-varying betweenness centrality, dynamic betweenness centrality, time-respecting betweenness |
| Verwandt | 6 | 6 |
| Zusammenfassung≠ | 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 Betweenness Centrality (TBC) extends classical betweenness centrality to time-stamped networks by counting how often a node lies on time-respecting shortest paths — paths that traverse edges in chronological order. It identifies nodes that act as temporal brokers, controlling information or resource flow as it evolves over time, rather than in a static snapshot. |
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