A cospectral construction for the generalized distance matrix

TL;DR

This paper introduces a cospectral construction for generalized distance matrices of graphs, including special cases like the exponential distance matrix, and explores conditions for cospectrality across different parameter values.

Contribution

It presents a novel cospectral construction method for generalized distance matrices and analyzes cospectrality conditions for the exponential distance matrix.

Findings

A cospectral construction similar to Godsil-McKay Switching.

An upper bound on q for cospectrality across all graphs of a given diameter.

Unique cospectral constructions for q=1/2.

Abstract

The generalized distance matrix of a graph is a matrix in which the $(i,j)$th entry is a function, $f$, of the distance between vertex $i$ and vertex $j$. Depending on the choice of $f$, this family of matrices includes both the adjacency matrix and the traditional distance matrix. We present a cospectral construction for the generalized distance matrix akin to Godsil-McKay Switching. We also investigate a special case of the generalized distance matrix: the exponential distance matrix, which is a matrix where every entry is a value $q$ raised to the power of the distance between the vertices. We give an upper bound on the values of $q$ needed to show a pair of graphs is cospectral for all values of $q$ corresponding to the diameter of the graphs. We also give cospectral constructions unique to value $q=1/2$.