Determining Geodesic Distance on Arbitrary-order Subdivision of Tree with Applications

TL;DR

This paper introduces new techniques for calculating geodesic distance and mean first-passage time on complex tree models, enabling more efficient analysis of diffusion processes on treelike structures.

Contribution

The paper develops generalized mapping techniques for exact formulas of geodesic distance and MFPT on subdivided tree models, surpassing previous matrix-based methods.

Findings

Derived exact formulas for geodesic distance and MFPT on subdivided trees.

Techniques are more general and easier to implement than existing methods.

Applied methods to Cayley and exponential trees, demonstrating effectiveness.

Abstract

The problem of how to estimate diffusion on a graph effectively is of importance both theoretically and practically. In this paper, we make use of two widely studied indices, geodesic distance and mean first-passage time ($MFPT$) for random walk, to consider such a problem on some treelike models of interest. To this end, we first introduce several types of operations, for instance, $m$th-order subdivision and ($1,m$)-star-fractal operation, to generate the potential candidate models. And then, we develop a class of novel techniques based on mapping for calculating the exact formulas of the both quantities above on our models. Compared to those previous tools including matrix-based methods for addressing the issue of this type, the techniques proposed here are more general mainly because we generalize the initial condition for creating these models. Meantime, in order to show applications of our techniques to many other treelike models, the two popularly discussed models, Cayley tree $C(t,n)$ and Exponential tree $\mathcal{T}(t;m)$, are chosen to serve as representatives. While their correspondence solutions have been reported, our techniques are more convenient to implement than those pre-existing ones and thus this certifies our statements strongly. Final, we distinguish the difference among results and attempt to make some heuristic explanations to the reasons why the phenomena appear.