方法对比

	多层 PageRank	多层网络分析
领域	网络分析	网络分析
方法族	Machine learning	Machine learning
起源年份	2015	2014
提出者	De Domenico, M.; Sole-Ribalta, A.; Arenas, A. et al.	Kivela, M.; Boccaletti, S. et al.
类型	Centrality measure (random-walk-based)	Structural network model
开创性文献	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.
别名	multiplex PageRank, layer-coupled PageRank, multilayer random walk centrality, MuxRank	multiplex networks, multi-layer network analysis, multilayer network analysis, MNA
相关	5	6
摘要	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.