Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Tīkla difūzijas analīze× | Starppriekšrocība (Betweenness Centrality)× | |
|---|---|---|
| Nozare | Tīklu analīze | Tīklu analīze |
| Saime | Machine learning | Machine learning |
| Izcelsmes gads≠ | 1927 (epidemic roots); network formalization 1990s–2000s | 1977 |
| Autors≠ | Kermack, W. O. & McKendrick, A. G. | Freeman, L. C. |
| Tips≠ | Simulation / analytical model | Centrality measure |
| Pirmavots≠ | 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 ↗ | Freeman, L. C. (1977). A set of measures of centrality based on betweenness. Sociometry, 40(1), 35–41. DOI ↗ |
| Citi nosaukumi | diffusion on networks, information diffusion, contagion spreading model, network propagation model | Freeman betweenness, BC, geodesic betweenness, shortest-path betweenness |
| Saistītās≠ | 5 | 6 |
| Kopsavilkums≠ | 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. | Betweenness centrality, formalized by Linton C. Freeman in 1977, measures how often a node lies on the shortest path connecting every other pair of nodes in a network. High-betweenness nodes act as bridges or brokers: removing them fragments the network into disconnected components more severely than removing any other nodes. |
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