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| Multilayer PageRank× | Centralità di Betweenness Multistrato× | |
|---|---|---|
| Campo | Analisi delle reti | Analisi delle reti |
| Famiglia | Machine learning | Machine learning |
| Anno di origine≠ | 2015 | 2013–2014 |
| Ideatore≠ | De Domenico, M.; Sole-Ribalta, A.; Arenas, A. et al. | De Domenico, M.; Kivelä, M.; Arenas, A. et al. |
| Tipo≠ | Centrality measure (random-walk-based) | Centrality measure (multilayer extension) |
| Fonte seminale≠ | 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 ↗ | 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 ↗ |
| Alias | multiplex PageRank, layer-coupled PageRank, multilayer random walk centrality, MuxRank | MBC, multilayer geodesic betweenness, tensorial betweenness centrality, interlayer betweenness centrality |
| Correlati | 5 | 5 |
| Sintesi≠ | 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. | 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. |
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