Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Tīkla difūzijas analīze× | Sociālo tīklu analīze× | |
|---|---|---|
| Nozare | Tīklu analīze | Tīklu analīze |
| Saime | Machine learning | Machine learning |
| Izcelsmes gads≠ | 1927 (epidemic roots); network formalization 1990s–2000s | 1934 (sociometry); 1994 (modern formalization) |
| Autors≠ | Kermack, W. O. & McKendrick, A. G. | Moreno, J.L.; formalized by Wasserman & Faust |
| Tips≠ | Simulation / analytical model | Structural/relational analysis framework |
| 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 ↗ | Wasserman, S. & Faust, K. (1994). Social Network Analysis: Methods and Applications. Cambridge University Press. ISBN: 978-0-521-38707-1 |
| Citi nosaukumi | diffusion on networks, information diffusion, contagion spreading model, network propagation model | SNA, network analysis, sociometric analysis, relational analysis |
| Saistītās | 5 | 5 |
| 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. | Social Network Analysis (SNA) is a structural method that maps and measures relationships and flows between people, groups, organizations, or other entities modeled as nodes connected by ties (edges). Rather than focusing on individual attributes, SNA reveals how the pattern of connections shapes behavior, influence, information flow, and outcomes within a system. |
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