方法对比
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| 贝叶斯网络扩散分析× | 贝叶斯随机图模型× | |
|---|---|---|
| 领域 | 网络分析 | 网络分析 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2010s | 2011 |
| 提出者≠ | Gomez Rodriguez, M.; Leskovec, J.; and related network science community | Caimo, A., & Friel, N. |
| 类型≠ | Probabilistic inference on network spreading processes | Bayesian statistical model for networks |
| 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 ↗ | Caimo, A., & Friel, N. (2011). Bayesian inference for exponential random graph models. Social Networks, 33(1), 41–55. DOI ↗ |
| 别名 | Bayesian diffusion model, probabilistic network diffusion, Bayesian spreading process inference, BNDA | Bayesian ERGM, Bayesian p-star model, Bayesian p* model, BERGM |
| 相关≠ | 5 | 4 |
| 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. | The Bayesian Exponential Random Graph Model (Bayesian ERGM or BERGM) extends the classical ERGM framework by placing prior distributions over the model parameters and using Markov chain Monte Carlo methods to obtain full posterior distributions. Introduced by Caimo and Friel (2011), it allows researchers to quantify parameter uncertainty and incorporate prior knowledge when modelling the structural features of social and other complex networks. |
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