Moments in graphs

TL;DR

This paper introduces a generalized graph invariant called the $ ho$-moment, providing formulas for its computation on complex graphs built via a graft product, and explores its applications in graph isomorphism and distance measures.

Contribution

It derives exact formulas for the $ ho$-moment of graphs formed by a graft product, extending previous invariants like Wiener index and degree distance, and offers methods to construct nonisomorphic graphs with identical $ ho$-moments.

Findings

Formulas for $ ho$-moment of graft product graphs.

Method to construct nonisomorphic graphs with same $ ho$-moment.

Explicit formulas for degree distance in trees and cycles.

Abstract

Let $G$ be a connected graph with vertex set $V$ and a {\em weight function} $\rho$ that assigns a nonnegative number to each of its vertices. Then, the {\em $\rho$-moment} of $G$ at vertex $u$ is defined to be $M_G^{\rho}(u)=\sum_{v\in V} \rho(v)\dist (u,v) $, where $\dist(\cdot,\cdot)$ stands for the distance function. Adding up all these numbers, we obtain the {\em $\rho$-moment of $G$}: $$ M_G^{\rho}=\sum_{u\in V}M_G^{\rho}(u)=1/2\sum_{u,v\in V}\dist(u,v)[\rho(u)+\rho(v)]. $$ This parameter generalizes, or it is closely related to, some well-known graph invariants, such as the {\em Wiener index} $W(G)$, when $\rho(u)=1/2$ for every $u\in V$, and the {\em degree distance} $D'(G)$, obtained when $\rho(u)=\delta(u)$, the degree of vertex $u$. In this paper we derive some exact formulas for computing the $\rho$-moment of a graph obtained by a general operation called graft product, which can be seen as a generalization of the hierarchical product, in terms of the corresponding $\rho$-moments of its factors. As a consequence, we provide a method for obtaining nonisomorphic graphs with the same $\rho$-moment for every $\rho$ (and hence with equal mean distance, Wiener index, degree distance, etc.). In the case when the factors are trees and/or cycles, techniques from linear algebra allow us to give formulas for the degree distance of their product.