Surgery principles for the spectral analysis of quantum graphs

TL;DR

This paper develops new spectral surgery principles for quantum graphs, showing how local modifications affect the Laplacian spectrum, and introduces a new eigenvalue estimate based on graph connectivity.

Contribution

It introduces novel spectral surgery techniques, including transplantation and unfolding, and extends eigenvalue inequalities for quantum graphs.

Findings

New transplantation and unfolding principles for spectral analysis.

A quantitative eigenvalue estimate based on the doubly connected part of a graph.

Generalizations of eigenvalue inequalities under graph modifications.

Abstract

We present a systematic collection of spectral surgery principles for the Laplacian on a metric graph with any of the usual vertex conditions (natural, Dirichlet or $\delta$-type), which show how various types of changes of a local or localised nature to a graph impact the spectrum of the Laplacian. Many of these principles are entirely new, these include "transplantation" of volume within a graph based on the behaviour of its eigenfunctions, as well as "unfolding" of local cycles and pendants. In other cases we establish sharp generalisations, extensions and refinements of known eigenvalue inequalities resulting from graph modification, such as vertex gluing, adjustment of vertex conditions and introducing new pendant subgraphs. To illustrate our techniques we derive a new eigenvalue estimate which uses the size of the doubly connected part of a metric graph to estimate the spectral gap. This quantitative isoperimetric-type inequality interpolates between two known estimates---one assuming the entire graph is doubly connected and the other making no connectivity assumption (and producing a weaker bound)---and includes them as special cases.