Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Svērtas tīklu difūzijas analīze× | Svērtā PageRank× | |
|---|---|---|
| Nozare | Tīklu analīze | Tīklu analīze |
| Saime | Machine learning | Machine learning |
| Izcelsmes gads | 2004 | 2004 |
| Autors≠ | Barrat, A.; Newman, M. E. J. | Xing, W. & Ghorbani, A. |
| Tips≠ | Network diffusion model | Centrality measure / ranking algorithm |
| Pirmavots≠ | Barrat, A., Barthelemy, 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 ↗ | 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 ↗ |
| Citi nosaukumi | WNDA, weighted diffusion process, edge-weighted spreading analysis, weighted information diffusion | WPR, weighted page rank, edge-weighted PageRank, strength-based PageRank |
| Saistītās | 6 | 6 |
| Kopsavilkums≠ | Weighted Network Diffusion Analysis models how information, influence, disease, or resources spread through a network whose edges carry quantitative strength values. By letting tie weights govern transition probabilities, the method produces more realistic spreading dynamics than binary-edge diffusion, revealing which high-traffic pathways dominate propagation in social, biological, and information networks. | 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. |
| ScholarGateDatu kopa ↗ |
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