方法对比
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| 多层介数中心性× | 中间性中心度× | |
|---|---|---|
| 领域 | 网络分析 | 网络分析 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2013–2014 | 1977 |
| 提出者≠ | De Domenico, M.; Kivelä, M.; Arenas, A. et al. | Freeman, L. C. |
| 类型≠ | Centrality measure (multilayer extension) | Centrality measure |
| 开创性文献≠ | 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 ↗ | Freeman, L. C. (1977). A set of measures of centrality based on betweenness. Sociometry, 40(1), 35–41. DOI ↗ |
| 别名 | MBC, multilayer geodesic betweenness, tensorial betweenness centrality, interlayer betweenness centrality | Freeman betweenness, BC, geodesic betweenness, shortest-path betweenness |
| 相关≠ | 5 | 6 |
| 摘要≠ | 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. | Betweenness centrality, formalized by Linton C. Freeman in 1977, measures how often a node lies on the shortest path connecting every other pair of nodes in a network. High-betweenness nodes act as bridges or brokers: removing them fragments the network into disconnected components more severely than removing any other nodes. |
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