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| Multilayer Degree Centrality× | Multiplex Hálózatok Elemzése× | |
|---|---|---|
| Tudományterület | Hálózatelemzés | Hálózatelemzés |
| Módszercsalád | Machine learning | Machine learning |
| Keletkezés éve≠ | 2013–2014 | 2014 |
| Megalkotó≠ | Kivelä, M.; De Domenico, M. et al. | Kivela, M.; Boccaletti, S. et al. |
| Típus≠ | Centrality measure for multilayer networks | Structural network model |
| Alapmű≠ | 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 ↗ |
| Alternatív nevek | multilayer degree, multiplex degree centrality, overlapping-layer degree centrality, MDC | multiplex networks, multi-layer network analysis, multilayer network analysis, MNA |
| Kapcsolódó | 6 | 6 |
| Összefoglaló≠ | 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. | Multiplex network analysis studies systems where the same set of nodes is connected by multiple distinct types of relationships, each represented as a separate network layer. By analyzing layers simultaneously rather than in isolation, it reveals how different relation types interact, reinforce each other, or compensate for one another across the same actors or entities. |
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