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| Multilayer PageRank× | Ugunduzi wa Jumuiya za Tabaka Nyingi× | |
|---|---|---|
| Nyanja | Uchanganuzi wa Mitandao | Uchanganuzi wa Mitandao |
| Familia | Machine learning | Machine learning |
| Mwaka wa asili≠ | 2015 | 2010–2014 |
| Mwanzilishi≠ | De Domenico, M.; Sole-Ribalta, A.; Arenas, A. et al. | Mucha, P. J. et al.; Kivela, M. et al. |
| Aina≠ | Centrality measure (random-walk-based) | Community detection algorithm for multilayer networks |
| Chanzo asilia≠ | 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 ↗ | 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 ↗ |
| Majina mbadala | multiplex PageRank, layer-coupled PageRank, multilayer random walk centrality, MuxRank | multilayer clustering, multiplex community detection, cross-layer community detection, MCD |
| Zinazohusiana | 5 | 5 |
| Muhtasari≠ | 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 community detection identifies groups of nodes that are densely connected across multiple types of relationships simultaneously. By coupling layers of a network — such as friendship, advice, and collaboration ties — it finds communities that are coherent not just within one relation type but across all of them, revealing structure that single-layer analysis would miss. |
| ScholarGateSeti ya data ↗ |
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