方法对比
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| 贝叶斯自我网络分析× | 贝叶斯随机块模型× | |
|---|---|---|
| 领域 | 网络分析 | 网络分析 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2010s | 2001–2014 |
| 提出者≠ | Various (Bayesian SNA tradition; Krivitsky, Kolaczyk, Handcock among key contributors) | Nowicki, K. & Snijders, T. A. B.; extended by Peixoto, T. P. |
| 类型≠ | Probabilistic network model | Probabilistic generative model with Bayesian inference |
| 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 ↗ | Peixoto, T. P. (2014). Efficient Monte Carlo and greedy heuristic for the inference of stochastic block models. Physical Review E, 89(1), 012804. DOI ↗ |
| 别名 | Bayesian personal network analysis, Bayesian egocentric network analysis, probabilistic ego network modeling, Bayesian egonet | Bayesian SBM, B-SBM, probabilistic block model, Bayesian community detection model |
| 相关 | 5 | 5 |
| 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 Stochastic Block Model (Bayesian SBM) is a principled probabilistic method for community detection in networks. It treats group membership as a latent variable and uses Bayesian inference to simultaneously recover block structure and select the number of communities, avoiding the resolution-limit bias that plagues modularity-based approaches. |
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