方法对比
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| 时态社群检测× | 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≠ | 2010 | 2004 |
| 提出者≠ | Mucha, P. J. et al. | Newman, M. E. J. & Girvan, M. |
| 类型≠ | Network clustering algorithm | Community detection / graph partitioning |
| 开创性文献≠ | Mucha, P. J., Richardson, T., Macon, K., Porter, M. A., & Onnela, J.-P. (2010). Community structure in time-dependent, multiscale, and multiplex networks. Science, 328(5980), 876–878. DOI ↗ | Newman, M. E. J., & Girvan, M. (2004). Finding and evaluating community structure in networks. Physical Review E, 69(2), 026113. DOI ↗ |
| 别名 | dynamic community detection, time-varying community detection, evolutionary community detection, longitudinal community detection | Q-modularity, community structure detection, network modularity optimization, graph partitioning by modularity |
| 相关≠ | 6 | 5 |
| 摘要≠ | Temporal community detection identifies cohesive groups (communities) in networks whose structure changes over time. By treating each time snapshot as a network layer and coupling consecutive layers, it reveals how communities form, merge, split, grow, or dissolve — turning a sequence of static snapshots into a continuous narrative of group evolution. | 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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