Machine learningNetwork science

Multilayer PageRank

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.

Open in MethodMindSoonVideoSoon

Read the full method

Members only

Sign in with a free account to read this section.

Sign in

Sources

  1. 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. DOI: 10.1038/ncomms7868
  2. Boccaletti, S., Bianconi, G., Criado, R., del Genio, C. I., Gomez-Gardenes, J., Romance, M., Sendina-Nadal, I., Wang, Z., & Zanin, M. (2014). The structure and dynamics of multilayer networks. Physics Reports, 544(1), 1–122. DOI: 10.1016/j.physrep.2014.07.001

Related methods

Referenced by

ScholarGateMultilayer PageRank (Multilayer PageRank (Centrality on Multiplex and Multilayer Networks)). Retrieved 2026-06-04 from https://scholargate.app/en/network-analysis/multilayer-pagerank