Zhenhua Yuan | Searcharxiv

arXiv2025

Transport Efficiency for Networks Obtained by Vertex Merging Operation

Transport is an important function of networks. Studying transport efficiency sheds light on the dynamic processes occurring within various underlying structures and offers a wide range of applications. To construct networks with different transport efficiencies, we focus on the networks obtained by vertex merging operation, which involves connecting multiple graphs through a single node. In this paper, we examine unbiased random walks on these networks and analyze their first-passage properties, including the mean first-passage time (MFPT), the mean trapping time (MTT), and the global-mean first-passage time (GFPT), which characterizes the transport (search) efficiency within the networks. We rigorously derive close-form solutions for these quantities. Results show that all these quantities are governed by the first-passage properties of the constituent components. Additionally, we propose a general method for optimizing the transport (search) efficiency by selecting a suitable node and adjusting the growth of the number of nodes in the subgraphs. We validate our findings using lollipop and barbell graphs. Our results indicate that for an arbitrary GFPT scaling exponent $α\in [1, 3]$, we can construct a network with GFPT scales with the network size $N$ as $\text{GFPT} \sim N^α$ through vertex merging operation. These conclusions provide valuable insights for designing and optimizing network structures.

nlin.CD↗

arXiv2022

The normalized Laplacian spectrum of $n$-polygon graphs and its applications

Given an arbitrary connected $G$, the $n$-polygon graph $τ_n(G)$ is obtained by adding a path with length $n$ $(n\geq 2)$ to each edge of graph $G$, and the iterated $n$-polygon graphs $τ_n^g(G)$ ($g\geq 0$), is obtained from the iteration $τ_n^g(G)=τ_n(τ_n^{g-1}(G))$, with initial condition $τ_n^0(G)=G$. In this paper, a method for calculating the eigenvalues of normalized Laplacian matrix for graph $τ_n(G)$ is presented if the eigenvalues of normalized Laplacian matrix for graph $G$ is given firstly. Then, the normalized Laplacian spectrums for the graph $τ_n(G)$ and the graphs $τ_n^g(G)$ ($g\geq 0$) can also be derived. Finally, as applications, we calculate the multiplicative degree-Kirchhoff index, Kemeny's constant and the number of spanning trees for the graph $τ_n(G)$ and the graphs $τ_n^g(G)$ by exploring their connections with the normalized Laplacian spectrum, exact results for these quantities are obtained.

math.CO↗