Volatilities analysis of first-passage time and first-return time on a small-world scale-free network

TL;DR

This paper analyzes the volatility of first-passage and first-return times on a small-world scale-free network, revealing large fluctuations in return times and more stable passage times, with implications for network dynamics understanding.

Contribution

It provides exact calculations of generating functions, distributions, and moments for FPT and FRT on the PSFW, highlighting differences in their fluctuations and reliability of mean estimates.

Findings

FRT fluctuations grow with network size and tend to infinity.

GFPT fluctuations remain bounded and stable for large networks.

FRT estimates are unreliable due to large fluctuations.

Abstract

In this paper, we study random walks on a small-world scale-free network, also called as pseudofractal scale-free web (PSFW), and analyze the volatilities of first passage time (FPT) and first return time (FRT) by using the variance and the reduced moment as the measures. Note that the FRT and FPT are deeply affected by the starting or target site. We don't intend to enumerate all the possible cases and analyze them. We only study the volatilities of FRT for a given hub (i.e., node with highest degree) and the volatilities of the global FPT (GFPT) to a given hub, which is the average of the FPTs for arriving at a given hub from any possible starting site selected randomly according to the equilibrium distribution of the Markov chain. Firstly, we calculate exactly the probability generating function of the GFPT and FRT based on the self-similar structure of the PSFW. Then, we calculate the probability distribution, the mean, the variance and reduced moment of the GFPT and FRT by using the generating functions as a tool. Results show that: the reduced moment of FRT grows with the increasing of the network order $N$ and tends to infinity while $N\rightarrow\infty$; but for the reduced moments of GFPT, it is almost a constant($\approx1.1605$) for large $N$. Therefore, on the PSFW of large size, the FRT has huge fluctuations and the estimate provided by MFRT is unreliable, whereas the fluctuations of the GFPT is much smaller and the estimate provided by its mean is more reliable. The method we propose can also be used to analyze the volatilities of FPT and FRT on other networks with self-similar structure, such as $(u, v)$ flowers and recursive scale-free trees.