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| 다층 차수 중심성× | 다층 근접 중심성× | |
|---|---|---|
| 분야 | 네트워크 분석 | 네트워크 분석 |
| 계열 | Machine learning | Machine learning |
| 기원 연도 | 2013–2014 | 2013–2014 |
| 창시자≠ | Kivelä, M.; De Domenico, M. et al. | Kivela, M. et al.; De Domenico, M. et al. |
| 유형 | Centrality measure for multilayer networks | Centrality measure for multilayer networks |
| 원전≠ | Kivelä, 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 ↗ |
| 별칭 | multilayer degree, multiplex degree centrality, overlapping-layer degree centrality, MDC | multilayer closeness, multi-layer closeness centrality, MLC, interlayer closeness centrality |
| 관련≠ | 6 | 5 |
| 요약≠ | Multilayer degree centrality extends the classic degree centrality measure to networks composed of multiple layers — such as networks representing different types of social ties, communication channels, or relationship contexts simultaneously. It quantifies how many connections a node has across one or all layers, revealing nodes that are influential not just in a single context but across the entire multi-relational structure. | 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. |
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