方法对比
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| 网络扩散分析× | 特征向量中心性× | |
|---|---|---|
| 领域 | 网络分析 | 网络分析 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 1927 (epidemic roots); network formalization 1990s–2000s | 1972 |
| 提出者≠ | Kermack, W. O. & McKendrick, A. G. | Bonacich, P. |
| 类型≠ | Simulation / analytical model | Centrality measure |
| 开创性文献≠ | 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 ↗ |
| 别名 | diffusion on networks, information diffusion, contagion spreading model, network propagation model | eigenvector centrality, EC, Bonacich centrality, power centrality |
| 相关≠ | 5 | 6 |
| 摘要≠ | 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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