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| Multilagsgradcentralitet× | Multilayer PageRank× | |
|---|---|---|
| Fagområde | Netværksanalyse | Netværksanalyse |
| Familie | Machine learning | Machine learning |
| Oprindelsesår≠ | 2013–2014 | 2015 |
| Ophavsperson≠ | Kivelä, M.; De Domenico, M. et al. | De Domenico, M.; Sole-Ribalta, A.; Arenas, A. et al. |
| Type≠ | Centrality measure for multilayer networks | Centrality measure (random-walk-based) |
| Oprindelig kilde≠ | 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 ↗ | De Domenico, M., Sole-Ribalta, A., Omodei, E., Gomez, S., & Arenas, A. (2015). Ranking in interconnected multilayer networks reveals versatile nodes. Nature Communications, 6, 6868. DOI ↗ |
| Aliasser | multilayer degree, multiplex degree centrality, overlapping-layer degree centrality, MDC | multiplex PageRank, layer-coupled PageRank, multilayer random walk centrality, MuxRank |
| Relaterede≠ | 6 | 5 |
| Resumé≠ | 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 PageRank extends the classic PageRank random-walk centrality to networks that contain multiple interconnected layers — such as a social network where people are connected simultaneously via friendship, professional ties, and online platforms. By allowing a virtual walker to jump both within and across layers, the algorithm identifies nodes that are influential across the entire multilayer structure, not just within any single layer. |
| ScholarGateDatasæt ↗ |
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