方法对比
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| 贝叶斯社会网络分析× | 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≠ | 2002 | 2011 |
| 提出者≠ | Hoff, P. D.; Raftery, A. E.; Handcock, M. S. | Caimo, A., & Friel, N. |
| 类型≠ | Probabilistic / Bayesian network model | Bayesian statistical model for networks |
| 开创性文献≠ | Hoff, P. D., Raftery, A. E., & Handcock, M. S. (2002). Latent space approaches to social network analysis. Journal of the American Statistical Association, 97(460), 1090–1098. DOI ↗ | Caimo, A., & Friel, N. (2011). Bayesian inference for exponential random graph models. Social Networks, 33(1), 41–55. DOI ↗ |
| 别名 | Bayesian SNA, Bayesian network modeling, probabilistic social network analysis, Bayesian relational modeling | Bayesian ERGM, Bayesian p-star model, Bayesian p* model, BERGM |
| 相关≠ | 5 | 4 |
| 摘要≠ | Bayesian Social Network Analysis applies Bayesian probabilistic inference to relational data, placing prior distributions over network parameters and updating them with observed tie data to yield full posterior distributions over structural features, tie probabilities, and latent actor positions. It enables principled uncertainty quantification in network models, making it especially valuable when data are sparse, partially observed, or subject to measurement error. | 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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