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| 多层紧密度中心性× | 多层介数中心性× | |
|---|---|---|
| 领域 | 网络分析 | 网络分析 |
| 方法族 | Machine learning | Machine learning |
| 起源年份 | 2013–2014 | 2013–2014 |
| 提出者≠ | Kivela, M. et al.; De Domenico, M. et al. | De Domenico, M.; Kivelä, M.; Arenas, A. et al. |
| 类型≠ | Centrality measure for multilayer networks | Centrality measure (multilayer extension) |
| 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 ↗ | De Domenico, M., Solé-Ribalta, A., Cozzo, E., Kivelä, M., Moreno, Y., Porter, M. A., Gómez, S., & Arenas, A. (2013). Mathematical formulation of multilayer networks. Physical Review X, 3(4), 041022. DOI ↗ |
| 别名 | multilayer closeness, multi-layer closeness centrality, MLC, interlayer closeness centrality | MBC, multilayer geodesic betweenness, tensorial betweenness centrality, interlayer betweenness centrality |
| 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 betweenness centrality extends the classical betweenness measure to networks with multiple types of relationships — or layers — by computing how often a node lies on shortest paths that can traverse any layer or switch between layers. It identifies brokers and bridges whose influence spans distinct interaction domains simultaneously. |
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