Selection of Random Walkers that Optimizes the Global Mean First-Passage Time for Search in Complex Networks

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Abstract

We design a method to optimize the global mean first-passage time (GMFPT) of multiple random walkers searching in complex networks for a general target, without specifying the property of the target node. According to the Laplace transformed formula of the GMFPT, we can equivalently minimize the overlap between the probability distribution of sites visited by the random walkers. We employ a mutation only genetic algorithm to solve this optimization problem using a population of walkers with different starting positions and a corresponding mutation matrix to modify them. The numerical experiments on two kinds of random networks (WS and BA) show satisfactory results in selecting the origins for the walkers to achieve minimum overlap. Our method thus provides guidance for setting up the search process by multiple random walkers on complex networks.

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Ding, M. C., & Szeto, K. Y. (2017). Selection of Random Walkers that Optimizes the Global Mean First-Passage Time for Search in Complex Networks. In Procedia Computer Science (Vol. 108, pp. 2423–2427). Elsevier B.V. https://doi.org/10.1016/j.procs.2017.05.015

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