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。
| 贝叶斯随机图模型× | 随机块模型× | |
|---|---|---|
| 领域 | 网络分析 | 网络分析 |
| 方法族≠ | Machine learning | Process / pipeline |
| 起源年份≠ | 2011 | 1983 |
| 提出者≠ | Caimo, A., & Friel, N. | — |
| 类型≠ | Bayesian statistical model for networks | Probabilistic generative graph model |
| 开创性文献≠ | Caimo, A., & Friel, N. (2011). Bayesian inference for exponential random graph models. Social Networks, 33(1), 41–55. DOI ↗ | Holland, P.W., Laskey, K.B. & Leinhardt, S. (1983). Stochastic Blockmodels: First Steps. Social Networks, 5(2), 109-137. DOI ↗ |
| 别名 | Bayesian ERGM, Bayesian p-star model, Bayesian p* model, BERGM | SBM, degree-corrected SBM, DCSBM, Stokastik Blok Modeli (SBM) |
| 相关≠ | 4 | 7 |
| 摘要≠ | 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. | The Stochastic Block Model (SBM), introduced by Holland, Laskey and Leinhardt (1983), is a probabilistic generative model for graphs that assigns nodes to latent blocks and parametrically estimates the connection probabilities between blocks. It is the foundational approach for community detection, core-periphery identification, and hierarchical structure discovery in network analysis. |
| ScholarGate数据集 ↗ |
|
|