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| Temporales PageRank× | Gerichtetes PageRank× | |
|---|---|---|
| Fachgebiet | Netzwerkanalyse | Netzwerkanalyse |
| Familie | Machine learning | Machine learning |
| Entstehungsjahr≠ | 2016 | 1998 |
| Urheber≠ | Rozenshtein, P. & Gionis, A. | Brin, S. & Page, L. |
| Typ≠ | Centrality / ranking algorithm for temporal networks | Iterative authority-scoring algorithm |
| 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 ↗ | Brin, S. & Page, L. (1998). The anatomy of a large-scale hypertextual Web search engine. Proceedings of the 7th International Conference on World Wide Web (WWW7), 107–117. Elsevier. link ↗ |
| Aliasnamen | TPR, time-aware PageRank, streaming PageRank, dynamic PageRank | PageRank, PR, Google PageRank, directed link analysis |
| Verwandt≠ | 6 | 5 |
| 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. | Directed PageRank is a link-based authority scoring algorithm that assigns importance scores to nodes in a directed graph by iteratively redistributing rank through outgoing edges. Introduced by Brin and Page in 1998 as the backbone of Google Search, it measures not just how many in-links a node has but how authoritative the nodes pointing to it are. |
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