DeepAI AI Chat
Log In Sign Up

Perron communicability and sensitivity of multilayer networks

by   Smahane El-Halouy, et al.
Kent State University
Sapienza University of Rome

Modeling complex systems that consist of different types of objects leads to multilayer networks, where nodes in the different layers represent different kind of objects. Nodes are connected by edges, which have positive weights. A multilayer network is associated with a supra-adjacency matrix. This paper investigates the sensitivity of the communicability in a multilayer network to perturbations of the network by studying the sensitivity of the Perron root of the supra-adjacency matrix. Our analysis sheds light on which edge weights to make larger to increase the communicability of the network, and which edge weights can be made smaller or set to zero without affecting the communicability significantly.


page 1

page 2

page 3

page 4


Edge Correlations in Multilayer Networks

Many recent developments in network analysis have focused on multilayer ...

Spectral properties of the Laplacian of temporal networks following a constant block Jacobi model

We study the behavior of the eigenvectors associated with the smallest e...

A new multilayer network construction via Tensor learning

Multilayer networks proved to be suitable in extracting and providing de...

Matrix function-based centrality measures for layer-coupled multiplex networks

Centrality measures identify the most important nodes in a complex netwo...

Eigenvector centrality for multilayer networks with dependent node importance

We present a novel approach for computing a variant of eigenvector centr...

TMM-Fast: A Transfer Matrix Computation Package for Multilayer Thin-Film Optimization

Achieving the desired optical response from a multilayer thin-film struc...