Asymptotic spectra of large (grid) graphs with a uniform local structure (part II): numerical applications

TL;DR

This paper explores the spectral properties of large grid-structured graphs with uniform local features, providing numerical applications in differential operator approximation and graph analysis.

Contribution

It extends the spectral analysis framework to practical numerical schemes and general large graphs with uniform local structures, supported by numerical examples.

Findings

Eigenvalue distribution characterized by a symbol function

Spectral gaps and clustering analyzed for large graphs

Numerical schemes effectively approximate differential operators

Abstract

In the current work we are concerned with sequences of graphs having a grid geometry, with a uniform local structure in a bounded domain $\Omega\subset {\mathbb R}^d$, $d\ge 1$. When $\Omega=[0,1]$, such graphs include the standard Toeplitz graphs and, for $\Omega=[0,1]^d$, the considered class includes $d$-level Toeplitz graphs. In the general case, the underlying sequence of adjacency matrices has a canonical eigenvalue distribution, in the Weyl sense, and it has been shown in the theoretical part of this work that we can associate to it a symbol $\boldsymbol{\mathfrak{f}}$. The knowledge of the symbol and of its basic analytical features provides key information on the eigenvalue structure in terms of localization, spectral gap, clustering, and global distribution. In the present paper, many different applications are discussed and various numerical examples are presented in order to underline the practical use of the developed theory. Tests and applications are mainly obtained from the approximation of differential operators via numerical schemes such as Finite Differences (FDs), Finite Elements (FEs), and Isogeometric Analysis (IgA). Moreover, we show that more applications can be taken into account, since the results presented here can be applied as well to study the spectral properties of adjacency matrices and Laplacian operators of general large graphs and networks, whenever the involved matrices enjoy a uniform local structure.