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.
ASJC Scopus subject areas
- Computer Networks and Communications
- Management Science and Operations Research
- Control and Optimization
- Computational Mathematics
- Applied Mathematics