Inferring Tree Structure with Hidden Traps from First Passage Times

TL;DR

This paper develops a method to infer the structure of finite Cayley trees from first passage time data, accounting for waiting times and traps, with applications in biological and spatial networks.

Contribution

It introduces a framework to reconstruct tree structures from FPT data, including cases with waiting times and traps, extending previous methods.

Findings

First two FPTFMs uniquely determine tree structure without waiting.

Waiting times and traps complicate structure inference, requiring additional analysis.

Fourier transform fitting enables structure reconstruction with power-law waiting times.

Abstract

Tracking the movement of tracer particles has long been a strategy for uncovering complex structures. Here, we study discrete-time random walks on finite Cayley trees to infer key parameters such as tree depth and geometric bias toward the root or leaves. By analyzing first passage properties, we show that the first two first-passage-time factorial moments (FPTFMs) uniquely determine the tree structure. However, if the random walker experiences waiting phases -- due to sticky branch walls or presence of traps -- this identification becomes nontrivial. We demonstrate that the generating function of the first passage time (FPT) distribution decomposes into contributions from the waiting time distribution and the random walk without waiting, leading to a nonlinear system of equations relating the factorial moments of the waiting time distribution and the FPTFMs of random walks with and without waiting. For geometrically distributed waiting times, additional moment measurements do not suffice, but unique determination of the structure is achieved by varying initial conditions or fitting the Fourier transform of the FPT distribution to measured data. The latter method remains effective also for power-law waiting time distributions, where higher-order FPTFMs are undefined. These results provide a framework for reconstructing tree-like networks from FPT data, with applications in biological transport and spatial networks.