Linganisha mbinu

	Multilayer PageRank	Uchanganuzi wa Mitandao Mingi (Multiplex Network Analysis)
Nyanja	Uchanganuzi wa Mitandao	Uchanganuzi wa Mitandao
Familia	Machine learning	Machine learning
Mwaka wa asili	2015	2014
Mwanzilishi	De Domenico, M.; Sole-Ribalta, A.; Arenas, A. et al.	Kivela, M.; Boccaletti, S. et al.
Aina	Centrality measure (random-walk-based)	Structural network model
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.	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.
Majina mbadala	multiplex PageRank, layer-coupled PageRank, multilayer random walk centrality, MuxRank	multiplex networks, multi-layer network analysis, multilayer network analysis, MNA
Zinazohusiana	5	6
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.	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.