Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Svērtā PageRank× | Svērtais pakāpes centralitāte× | |
|---|---|---|
| Nozare | Tīklu analīze | Tīklu analīze |
| Saime | Machine learning | Machine learning |
| Izcelsmes gads | 2004 | 2004 |
| Autors≠ | Xing, W. & Ghorbani, A. | Barrat, A.; Barthélemy, M.; Pastor-Satorras, R.; Vespignani, A. |
| Tips≠ | Centrality measure / ranking algorithm | Centrality measure for weighted networks |
| Pirmavots≠ | Xing, W., & Ghorbani, A. (2004). Weighted PageRank algorithm. Proceedings of the Second Annual Conference on Communication Networks and Services Research (CNSR '04), pp. 305–314. IEEE. DOI ↗ | Barrat, A., Barthélemy, M., Pastor-Satorras, R., & Vespignani, A. (2004). The architecture of complex weighted networks. Proceedings of the National Academy of Sciences, 101(11), 3747–3752. DOI ↗ |
| Citi nosaukumi | WPR, weighted page rank, edge-weighted PageRank, strength-based PageRank | node strength, strength centrality, weighted node degree, WDC |
| Saistītās | 6 | 6 |
| Kopsavilkums≠ | Weighted PageRank extends the classic PageRank algorithm to networks where edges carry different strengths or frequencies, distributing importance proportionally to both incoming and outgoing edge weights rather than treating all links equally. This makes it substantially more informative than binary PageRank in any network where connection strength matters. | Weighted degree centrality — also called node strength — extends the classic degree centrality measure to networks whose edges carry numeric weights. Instead of simply counting a node's connections, it sums the weights of all edges incident to that node, capturing both the volume and the intensity of a node's ties in a single, interpretable score. |
| ScholarGateDatu kopa ↗ |
|
|