Graph product multilayer networks: Spectral properties and applications

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

This article aims to establish theoretical foundations of graph product multilayer networks (GPMNs), a family of multilayer networks that can be obtained as a graph product of two or more factor networks. Cartesian, direct (tensor), and strong product operators are considered, and then generalized. We first describe mathematical relationships between GPMNs and their factor networks regarding their degree/strength, adjacency, and Laplacian spectra, and then show that those relationships can still hold for non-simple and generalized GPMNs. Applications of GPMNs are discussed in three areas: predicting epidemic thresholds, modelling propagation in non-trivial space and time, and analysing higher-order properties of self-similar networks. Directions of future research are also discussed.

Original languageEnglish
Pages (from-to)430-447
Number of pages18
JournalJournal of Complex Networks
Volume6
Issue number3
DOIs
Publication statusPublished - 2018 Jul 1

Fingerprint

Graph Products
Spectral Properties
Multilayer
Multilayers
Tensors
Strong Product
Laplacian Spectrum
Graph
Adjacency
Direct Product
Cartesian
Tensor Product
Propagation
Higher Order

Keywords

  • Degree/adjacency/Laplacian spectra
  • Epidemic thresholds
  • Graph product
  • Multilayer networks
  • Propagation
  • Self-similar networks

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Management Science and Operations Research
  • Control and Optimization
  • Computational Mathematics
  • Applied Mathematics

Cite this

Graph product multilayer networks : Spectral properties and applications. / Sayama, Hiroki.

In: Journal of Complex Networks, Vol. 6, No. 3, 01.07.2018, p. 430-447.

Research output: Contribution to journalArticle

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