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| Dynamic Stochastic Block Model× | 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≠ | Yang, T.; Chi, Y.; Zhu, S.; Gong, Y.; Jin, R. | Newman, M. E. J. & Girvan, M. |
| 类型≠ | Generative probabilistic model | Community detection / graph partitioning |
| 开创性文献≠ | Yang, T., Chi, Y., Zhu, S., Gong, Y., & Jin, R. (2011). Detecting communities and their evolutions in dynamic social networks — a Bayesian approach. Machine Learning, 82(2), 157–189. DOI ↗ | Newman, M. E. J., & Girvan, M. (2004). Finding and evaluating community structure in networks. Physical Review E, 69(2), 026113. DOI ↗ |
| 别名 | DSBM, dynamic SBM, time-varying stochastic block model, temporal block model | Q-modularity, community structure detection, network modularity optimization, graph partitioning by modularity |
| 相关 | 5 | 5 |
| 摘要≠ | The Dynamic Stochastic Block Model (DSBM) is a generative probabilistic framework that extends the static stochastic block model to networks observed across multiple time points. It jointly models community membership and community evolution, allowing researchers to detect and track latent groups and their structural changes over time in longitudinal network data. | 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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