Comparer des méthodes

	PageRank Multicouche	Détection de communautés multicouches
Domaine	Analyse de réseaux	Analyse de réseaux
Famille	Machine learning	Machine learning
Année d'origine	2015	2010–2014
Auteur d'origine	De Domenico, M.; Sole-Ribalta, A.; Arenas, A. et al.	Mucha, P. J. et al.; Kivela, M. et al.
Type	Centrality measure (random-walk-based)	Community detection algorithm for multilayer networks
Source fondatrice	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.
Alias	multiplex PageRank, layer-coupled PageRank, multilayer random walk centrality, MuxRank	multilayer clustering, multiplex community detection, cross-layer community detection, MCD
Apparentées	5	5
Résumé	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.