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| 多層近接中心性× | 多層媒介中心性 (Multilayer Betweenness Centrality)× | |
|---|---|---|
| 分野 | ネットワーク分析 | ネットワーク分析 |
| 系統 | Machine learning | Machine learning |
| 提唱年 | 2013–2014 | 2013–2014 |
| 提唱者≠ | Kivela, M. et al.; De Domenico, M. et al. | De Domenico, M.; Kivelä, M.; Arenas, A. et al. |
| 種類≠ | Centrality measure for multilayer networks | Centrality measure (multilayer extension) |
| 原典≠ | 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 ↗ |
| 別名 | multilayer closeness, multi-layer closeness centrality, MLC, interlayer closeness centrality | MBC, multilayer geodesic betweenness, tensorial betweenness centrality, interlayer betweenness centrality |
| 関連 | 5 | 5 |
| 概要≠ | 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. |
| ScholarGateデータセット ↗ |
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