方法对比
并排查看您选择的方法；存在差异的行会高亮显示。
| 动态指数随机图模型× | 网络扩散分析× | |
|---|---|---|
| 领域 | 网络分析 | 网络分析 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2010–2014 | 1927 (epidemic roots); network formalization 1990s–2000s |
| 提出者≠ | Hanneke, Fu & Xing; Krivitsky & Handcock | Kermack, W. O. & McKendrick, A. G. |
| 类型≠ | Probabilistic graphical model (temporal) | Simulation / analytical model |
| 开创性文献≠ | Hanneke, S., Fu, W., & Xing, E. P. (2010). Discrete temporal models of social networks. Electronic Journal of Statistics, 4, 585–605. 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 ↗ |
| 别名 | TERGM, Temporal ERGM, Dynamic ERGM, STERGM | diffusion on networks, information diffusion, contagion spreading model, network propagation model |
| 相关≠ | 4 | 5 |
| 摘要≠ | The Dynamic Exponential Random Graph Model (TERGM / STERGM) extends the classic ERGM framework to panel network data, modeling how a network's ties form and dissolve over time as a function of structural tendencies, nodal attributes, and the network's own past state. It provides statistically principled inference about longitudinal network change. | 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. |
| ScholarGate数据集 ↗ |
|
|