方法对比
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| 多层社区检测× | 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≠ | 2010–2014 | 2004 |
| 提出者≠ | Mucha, P. J. et al.; Kivela, M. et al. | Newman, M. E. J. & Girvan, M. |
| 类型≠ | Community detection algorithm for multilayer networks | Community detection / graph partitioning |
| 开创性文献≠ | Kivela, M., Arenas, A., Barthelemy, M., Gleeson, J. P., Moreno, Y., & Porter, M. A. (2014). Multilayer networks. Journal of Complex Networks, 2(3), 203–271. DOI ↗ | Newman, M. E. J., & Girvan, M. (2004). Finding and evaluating community structure in networks. Physical Review E, 69(2), 026113. DOI ↗ |
| 别名 | multilayer clustering, multiplex community detection, cross-layer community detection, MCD | Q-modularity, community structure detection, network modularity optimization, graph partitioning by modularity |
| 相关 | 5 | 5 |
| 摘要≠ | Multilayer community detection identifies groups of nodes that are densely connected across multiple types of relationships simultaneously. By coupling layers of a network — such as friendship, advice, and collaboration ties — it finds communities that are coherent not just within one relation type but across all of them, revealing structure that single-layer analysis would miss. | 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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