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Temporal PageRank

Temporal PageRank laiendab klassikalist PageRank algoritmi ajas muutuvatele võrkudele, arvestades interaktsioonide värskust ja järjekorda. Servad kaalutakse lagunemisfunktsiooniga nii, et hiljutised kontaktid annavad sõlme skoori rohkem panuse kui vanad. Tulemuseks on dünaamiline tähtsuse järjestus, mis tabab, kes on mõjukas praegu, mitte kogu võrgu ajaloo jooksul.

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Ainult liikmetele

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Logi sisse

Method map

The neighbourhood of related methods — select a node to explore.

Allikad

  1. 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: 10.1007/978-3-319-46227-1_42
  2. Lerman, K. & Ghosh, R. (2010). Information Contagion: An Empirical Study of the Spread of News on Digg and Twitter Social Networks. In Proceedings of the Fourth International AAAI Conference on Weblogs and Social Media (ICWSM), pp. 90–97. AAAI Press. link

Kuidas sellele lehele viidata

ScholarGate. (2026, June 3). Temporal PageRank (Time-Aware Node Importance Ranking in Temporal Networks). ScholarGate. https://scholargate.app/et/network-analysis/temporal-pagerank

Which method?

Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.

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Sellele viitavad

ScholarGateTemporal PageRank (Temporal PageRank (Time-Aware Node Importance Ranking in Temporal Networks)). Loetud 2026-06-15 aadressilt https://scholargate.app/et/network-analysis/temporal-pagerank · Andmestik: https://doi.org/10.5281/zenodo.20539026