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| 贝叶斯随机图模型× | 模块度分析× | |
|---|---|---|
| 领域 | 网络分析 | 网络分析 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2011 | 2004 |
| 提出者≠ | Caimo, A., & Friel, N. | Newman, M. E. J. & Girvan, M. |
| 类型≠ | Bayesian statistical model for networks | Community detection / graph partitioning |
| 开创性文献≠ | Caimo, A., & Friel, N. (2011). Bayesian inference for exponential random graph models. Social Networks, 33(1), 41–55. DOI ↗ | Newman, M. E. J., & Girvan, M. (2004). Finding and evaluating community structure in networks. Physical Review E, 69(2), 026113. DOI ↗ |
| 别名 | Bayesian ERGM, Bayesian p-star model, Bayesian p* model, BERGM | Q-modularity, community structure detection, network modularity optimization, graph partitioning by modularity |
| 相关≠ | 4 | 5 |
| 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. | Modularity analysis is a network science method, formalized by Newman and Girvan in 2004, that detects community structure in graphs by measuring whether edges are more concentrated within groups than expected by chance. Its scalar quality index Q guides algorithms that partition nodes into cohesive clusters, making it the most widely adopted framework for community detection in social, biological, and technological networks. |
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