A tensor formalism for multilayer network centrality measures using the Einstein product

10/17/2022
by   Smahane El-Halouy, et al.
0

Complex systems that consist of different kinds of entities that interact in different ways can be modeled by multilayer networks. This paper uses the tensor formalism with the Einstein tensor product to model this type of networks. Several centrality measures, that are well known for single-layer networks, are extended to multilayer networks using tensors and their properties are investigated. In particular, subgraph centrality based on the exponential and resolvent of a tensor are considered. Krylov subspace methods are introduced for computing approximations of different measures for large multilayer networks.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
04/09/2023

Spectral computation with third-order tensors using the t-product

The tensor t-product, introduced by Kilmer and Martin [26], is a powerfu...
research
04/11/2020

A new multilayer network construction via Tensor learning

Multilayer networks proved to be suitable in extracting and providing de...
research
02/28/2019

A Multilayer Structure Facilitates the Production of Antifragile Systems in Boolean Network Models

Antifragility is a property to not only resist stress and but also to be...
research
09/07/2019

Efficient Community Detection in Boolean Composed Multiplex Networks

Networks (or graphs) are used to model the dyadic relations between enti...
research
02/07/2020

Universal Equivariant Multilayer Perceptrons

Group invariant and equivariant Multilayer Perceptrons (MLP), also known...
research
05/15/2023

Towards efficient multilayer network data management

Real-world multilayer networks can be very large and there can be multip...
research
03/19/2019

Algorithmic complexity of multiplex networks

Multilayer networks preserve full information about the different intera...

Please sign up or login with your details

Forgot password? Click here to reset