قارن الطرق

	PageRank متعدد الطبقات	اكتشاف المجتمعات متعددة الطبقات
المجال	تحليل الشبكات	تحليل الشبكات
العائلة	Machine learning	Machine learning
سنة النشأة	2015	2010–2014
صاحب الطريقة	De Domenico, M.; Sole-Ribalta, A.; Arenas, A. et al.	Mucha, P. J. et al.; Kivela, M. et al.
النوع	Centrality measure (random-walk-based)	Community detection algorithm for multilayer networks
المصدر التأسيسي	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.
الأسماء البديلة	multiplex PageRank, layer-coupled PageRank, multilayer random walk centrality, MuxRank	multilayer clustering, multiplex community detection, cross-layer community detection, MCD
ذات صلة	5	5
الملخص	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.