方法对比
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| 贝叶斯双模网络分析× | 贝叶斯社群侦测× | |
|---|---|---|
| 领域 | 网络分析 | 网络分析 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 1997–2010s | 2001–2014 |
| 提出者≠ | Borgatti & Everett (two-mode SNA); Bayesian extensions by multiple authors | Nowicki, K. & Snijders, T. A. B. (formal Bayesian framing); extended by Peixoto, T. P. |
| 类型≠ | Probabilistic network model | Probabilistic generative model / inference |
| 开创性文献≠ | Borgatti, S. P., & Everett, M. G. (1997). Network analysis of 2-mode data. Social Networks, 19(3), 243–269. 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 bipartite network analysis, probabilistic two-mode network analysis, Bayesian affiliation network analysis, Bayesian two-mode SNA | Bayesian graph clustering, probabilistic community detection, Bayesian stochastic block model community detection, Bayesian network partitioning |
| 相关 | 5 | 5 |
| 摘要≠ | Bayesian two-mode network analysis applies probabilistic Bayesian inference to bipartite (two-mode) networks — graphs linking two distinct sets of nodes such as actors and events, authors and papers, or consumers and products. By placing priors over tie probabilities and structural parameters, analysts obtain uncertainty estimates around centrality, community membership, and projection metrics rather than single-point estimates. | Bayesian community detection infers latent group structure in networks by treating community membership as unobserved variables and using Bayesian inference — typically via Markov chain Monte Carlo or variational methods — to compute a posterior distribution over all plausible partitions. Unlike modularity optimisation, it selects the number of communities from data and provides principled uncertainty estimates for every node assignment. |
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