Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Bayesiešu PageRank× | Temporālais PageRank× | |
|---|---|---|
| Nozare | Tīklu analīze | Tīklu analīze |
| Saime | Machine learning | Machine learning |
| Izcelsmes gads≠ | 1999 (PageRank); 2000s (Bayesian extension) | 2016 |
| Autors≠ | Page, L. & Brin, S. (PageRank); Bayesian extension by multiple authors | Rozenshtein, P. & Gionis, A. |
| Tips≠ | Probabilistic centrality measure | Centrality / ranking algorithm for temporal networks |
| Pirmavots≠ | Page, L., Brin, S., Motwani, R., & Winograd, T. (1999). The PageRank citation ranking: Bringing order to the web. Stanford InfoLab Technical Report. link ↗ | 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 ↗ |
| Citi nosaukumi | Bayesian PR, probabilistic PageRank, uncertainty-aware PageRank, stochastic PageRank | TPR, time-aware PageRank, streaming PageRank, dynamic PageRank |
| Saistītās | 6 | 6 |
| Kopsavilkums≠ | Bayesian PageRank extends the classic PageRank algorithm by embedding it within a Bayesian probabilistic framework. Instead of returning a single deterministic rank score for each node, it quantifies uncertainty over rank estimates — particularly valuable when the network is incomplete, noisy, or observed with error. It is used in web analysis, citation networks, and social network research where rank uncertainty matters. | 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. |
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