Explicit determination of mean first-passage time for random walks on deterministic uniform recursive trees

TL;DR

This paper analytically determines the mean first-passage time and trapping times in deterministic uniform recursive trees, revealing how these quantities scale with network size and trapping location, advancing understanding of random walks on complex networks.

Contribution

It provides exact formulas for MFPT and ATT in DURTs, linking spectral graph theory with network dynamics, and highlights the impact of trap placement on trapping efficiency.

Findings

MFPT scales as N ln N for large networks

ATT scales linearly with network size N

Trap location significantly affects trapping time

Abstract

The determination of mean first-passage time (MFPT) for random walks in networks is a theoretical challenge, and is a topic of considerable recent interest within the physics community. In this paper, according to the known connections between MFPT, effective resistance, and the eigenvalues of graph Laplacian, we first study analytically the MFPT between all node pairs of a class of growing treelike networks, which we term deterministic uniform recursive trees (DURTs), since one of its particular cases is a deterministic version of the famous uniform recursive tree. The interesting quantity is determined exactly through the recursive relation of the Laplacian spectra obtained from the special construction of DURTs. The analytical result shows that the MFPT between all couples of nodes in DURTs varies as $N \ln N$ for large networks with node number $N$. Second, we study trapping on a particular network of DURTs, focusing on a special case with the immobile trap positioned at a node having largest degree. We determine exactly the average trapping time (ATT) that is defined as the average of FPT from all nodes to the trap. In contrast to the scaling of the MFPT, the leading behavior of ATT is a linear function of $N$. Interestingly, we show that the behavior for ATT of the trapping problem is related to the trapping location, which is in comparison with the phenomenon of trapping on fractal T-graph although both networks exhibit treestructure. Finally, we believe that the methods could open the way to exactly calculate the MFPT and ATT in a wide range of deterministic media.