Parametric controllability of the personalized PageRank: Classic model vs biplex approach

DOI10.1063/1.5128567zbMath1432.91094OpenAlexW3004461998WikidataQ89953365 ScholiaQ89953365MaRDI QIDQ5220490

Miguel Romance, Francisco Pedroche, Esther García, Julio Flores

Publication date: 26 March 2020

Published in: Chaos: An Interdisciplinary Journal of Nonlinear Science (Search for Journal in Brave)

Full work available at URL: http://hdl.handle.net/10251/160976

Mathematics Subject Classification ID

Social networks; opinion dynamics (91D30) Small world graphs, complex networks (graph-theoretic aspects) (05C82) Stochastic matrices (15B51)

Uses Software

• PrimAlign

Cites Work

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• On the shortest spanning subtree of a graph and the traveling salesman problem
• Towards Google matrix of brain
• Accounting for the role of long walks on networks via a new matrix function
• PageRank centrality for temporal networks
• Off-diagonal low-rank preconditioner for difficult PageRank problems
• A GMRES-power algorithm for computing PageRank problems
• Sharp estimates for the personalized multiplex PageRank
• Random walks and diffusion on networks
• Using PageRank for non-personalized default rankings in dynamic markets
• The general inner-outer iteration method based on regular splittings for the PageRank problem
• Black hole metric: overcoming the PageRank normalization problem
• Non-backtracking PageRank: from the classic model to Hashimoto matrices
• On the localization of the personalized PageRank of complex networks
• A new status index derived from sociometric analysis
• Emergence of Scaling in Random Networks
• Eigenvector centrality of nodes in multiplex networks
• A new framework for dynamical models on multiplex networks
• Approximating PageRank from In-Degree
• A biplex approach to PageRank centrality: From classic to multiplex networks
• Deeper Inside PageRank
• Axioms for Centrality
• Three Hypergraph Eigenvector Centralities
• Collective dynamics of ‘small-world’ networks
• Networks

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