Estimating random walk centrality in networks

Random walk centrality (equivalently, the accessibility index) for the states of a time homogeneous irreducible Markov chain on a finite state space is considered. It is known that the accessibility index for a particular state can be written in terms of the first and second moments of the first return time to that state. Based on that observation, the problem of estimating the random walk centrality of a state is approached by taking realizations of the Markov chain, and then statistically estimating the first two moments of the corresponding first return time. In addition to the estimate of the random walk centrality, this method also yields the standard error, the bias and a confidence interval for that estimate. For the case that the directed graph of the transition matrix for the Markov chain has a cut-point, an alternate strategy for computing the random walk centrality is outlined that may be of use when the centrality values are of interest for only some of the states. In order to illustrate the effectiveness of the results, estimates of the random walk centrality arising from random walks for several directed and undirected graphs are discussed. (C) 2019 Elsevier B.V. All rights reserved.