Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Temporālais PageRank× | Tīkla difūzijas analīze× | |
|---|---|---|
| Nozare | Tīklu analīze | Tīklu analīze |
| Saime | Machine learning | Machine learning |
| Izcelsmes gads≠ | 2016 | 1927 (epidemic roots); network formalization 1990s–2000s |
| Autors≠ | Rozenshtein, P. & Gionis, A. | Kermack, W. O. & McKendrick, A. G. |
| Tips≠ | Centrality / ranking algorithm for temporal networks | Simulation / analytical model |
| Pirmavots≠ | 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 ↗ | Kermack, W. O. & McKendrick, A. G. (1927). A contribution to the mathematical theory of epidemics. Proceedings of the Royal Society of London A, 115(772), 700–721. DOI ↗ |
| Citi nosaukumi | TPR, time-aware PageRank, streaming PageRank, dynamic PageRank | diffusion on networks, information diffusion, contagion spreading model, network propagation model |
| Saistītās≠ | 6 | 5 |
| Kopsavilkums≠ | 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. | Network diffusion analysis models how information, diseases, behaviors, or innovations spread across a graph of nodes and edges. Drawing on classical epidemic theory (SI, SIR, SIS) and modern network science, it tracks which nodes become infected, how quickly, and whether the spread reaches a global cascade or dies out locally. |
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