方法对比
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| 贝叶斯自我网络分析× | 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≠ | 2010s | 2011 |
| 提出者≠ | Various (Bayesian SNA tradition; Krivitsky, Kolaczyk, Handcock among key contributors) | Caimo, A., & Friel, N. |
| 类型≠ | Probabilistic network model | Bayesian statistical model for networks |
| 开创性文献≠ | Krivitsky, P. N., & Kolaczyk, E. D. (2015). On the question of effective sample size in network modeling: An asymptotic inquiry. Statistical Science, 30(2), 184–198. DOI ↗ | Caimo, A., & Friel, N. (2011). Bayesian inference for exponential random graph models. Social Networks, 33(1), 41–55. DOI ↗ |
| 别名 | Bayesian personal network analysis, Bayesian egocentric network analysis, probabilistic ego network modeling, Bayesian egonet | Bayesian ERGM, Bayesian p-star model, Bayesian p* model, BERGM |
| 相关≠ | 5 | 4 |
| 摘要≠ | Bayesian ego network analysis applies probabilistic inference to ego-centered (personal) network data, combining a likelihood model for the ego's local network with prior distributions over network parameters. The result is a full posterior distribution that quantifies uncertainty about structural features such as alter composition, tie density, and network size — rather than producing point estimates alone. | 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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