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| Netzwerkdiffusionsanalyse× | Eigenvektor-Zentralität× | |
|---|---|---|
| Fachgebiet | Netzwerkanalyse | Netzwerkanalyse |
| Familie | Machine learning | Machine learning |
| Entstehungsjahr≠ | 1927 (epidemic roots); network formalization 1990s–2000s | 1972 |
| Urheber≠ | Kermack, W. O. & McKendrick, A. G. | Bonacich, P. |
| Typ≠ | Simulation / analytical model | Centrality measure |
| Wegweisende Quelle≠ | 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 ↗ | Bonacich, P. (1972). Factoring and weighting approaches to status scores and clique identification. Journal of Mathematical Sociology, 2(1), 113–120. DOI ↗ |
| Aliasnamen | diffusion on networks, information diffusion, contagion spreading model, network propagation model | eigenvector centrality, EC, Bonacich centrality, power centrality |
| Verwandt≠ | 5 | 6 |
| Zusammenfassung≠ | 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. | Eigenvector centrality, introduced by Bonacich in 1972, measures a node's influence by considering not just how many neighbors it has, but how influential those neighbors are. A node scores highly if it is connected to other high-scoring nodes, making it a recursive, globally-aware measure of structural importance in a network. |
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