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| 多层紧密度中心性× | 多层社区检测× | |
|---|---|---|
| 领域 | 网络分析 | 网络分析 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2013–2014 | 2010–2014 |
| 提出者≠ | Kivela, M. et al.; De Domenico, M. et al. | Mucha, P. J. et al.; Kivela, M. et al. |
| 类型≠ | Centrality measure for multilayer networks | Community detection algorithm for multilayer networks |
| 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 ↗ | 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 ↗ |
| 别名 | multilayer closeness, multi-layer closeness centrality, MLC, interlayer closeness centrality | multilayer clustering, multiplex community detection, cross-layer community detection, MCD |
| 相关 | 5 | 5 |
| 摘要≠ | Multilayer closeness centrality extends the classical closeness centrality measure to networks that contain multiple types of relationships or interaction contexts (layers). Rather than treating each layer in isolation, it computes how quickly a node can reach all others by traversing any combination of available layers, revealing nodes that are structurally efficient connectors across the full network system. | 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. |
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