Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Tuvuma centralitāte× | Tīkla difūzijas analīze× | |
|---|---|---|
| Nozare | Tīklu analīze | Tīklu analīze |
| Saime | Machine learning | Machine learning |
| Izcelsmes gads≠ | 1950 (formalized 1979) | 1927 (epidemic roots); network formalization 1990s–2000s |
| Autors≠ | Bavelas, A.; formalized by Freeman, L. C. | Kermack, W. O. & McKendrick, A. G. |
| Tips≠ | Node-level centrality index | Simulation / analytical model |
| Pirmavots≠ | Freeman, L. C. (1979). Centrality in social networks: Conceptual clarification. Social Networks, 1(3), 215–239. 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 | closeness, farness-based centrality, geodesic closeness, normalized closeness centrality | diffusion on networks, information diffusion, contagion spreading model, network propagation model |
| Saistītās≠ | 6 | 5 |
| Kopsavilkums≠ | Closeness centrality measures how quickly a node can reach all others in a network by computing the inverse of its average shortest-path distance to every other node. First described by Bavelas (1950) and formally unified by Freeman (1979), it identifies nodes that can spread information or resources efficiently across the entire graph — not merely nodes with many direct contacts. | 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. |
| ScholarGateDatu kopa ↗ |
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