方法对比
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| 贝叶斯网络扩散分析× | 时间网络扩散分析× | |
|---|---|---|
| 领域 | 网络分析 | 网络分析 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2010s | 2012 |
| 提出者≠ | Gomez Rodriguez, M.; Leskovec, J.; and related network science community | Holme, P. & Saramäki, J. |
| 类型≠ | Probabilistic inference on network spreading processes | Network analysis framework |
| 开创性文献≠ | Gomez Rodriguez, M., Leskovec, J., & Scholkopf, B. (2012). Structure and Dynamics of Information Pathways in Online Media. Proceedings of the 6th ACM International Conference on Web Search and Data Mining (WSDM), 23–32. DOI ↗ | Holme, P. & Saramäki, J. (2012). Temporal networks. Physics Reports, 519(3), 97–125. DOI ↗ |
| 别名 | Bayesian diffusion model, probabilistic network diffusion, Bayesian spreading process inference, BNDA | TNDA, dynamic network diffusion, time-varying network spreading, diffusion on temporal networks |
| 相关 | 5 | 5 |
| 摘要≠ | Bayesian Network Diffusion Analysis applies Bayesian probabilistic inference to the study of how information, diseases, behaviors, or innovations propagate through a network. By placing priors over diffusion parameters and updating them with observed cascade data, it quantifies transmission rates, identifies influential spreaders, reconstructs latent propagation pathways, and provides full uncertainty estimates — all within a principled statistical framework. | Temporal Network Diffusion Analysis studies how information, disease, influence, or other contagions spread through networks whose structure changes over time. By modeling edges as time-stamped contacts rather than static links, it captures the critical role of timing and ordering in determining which nodes get reached, how fast, and through which pathways — producing conclusions that static network models systematically miss. |
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