পদ্ধতির তুলনা করুন

	মাল্টিলেয়ার পেজর‍্যাঙ্ক	মাল্টিপ্লেক্স নেটওয়ার্ক বিশ্লেষণ
ক্ষেত্র	নেটওয়ার্ক বিশ্লেষণ	নেটওয়ার্ক বিশ্লেষণ
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	2015	2014
প্রবর্তক	De Domenico, M.; Sole-Ribalta, A.; Arenas, A. et al.	Kivela, M.; Boccaletti, S. et al.
ধরন	Centrality measure (random-walk-based)	Structural network model
মৌলিক উৎস	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.
অপর নাম	multiplex PageRank, layer-coupled PageRank, multilayer random walk centrality, MuxRank	multiplex networks, multi-layer network analysis, multilayer network analysis, MNA
সম্পর্কিত	5	6
সারসংক্ষেপ	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.