An inverse problem for fractional random walks on finite graphs

Abstract We study an inverse problem on a finite connected graph G = ( X , E ) , on whose vertices a conductivity γ is defined. Our data consists in a sequence of partial observations of a fractional random walk on G . The observations are partial in the sense that they are limited to a fixed, observable subset B ⊆ X , while the random walk is fractional in the sense that it allows long jumps with a probability P decreasing as a fractional power of the distance along the graph. The transition probability P also depends on γ . We show that this kind of random walk data allows for the determination of a gauge class to which the transition probability matrix P belongs, which we discuss. Moreover, we show that if the transition probability matrix P is itself known, then the amount of vertices | X | , the edge set E and the conductivity γ (up to a positive factor) can be recovered. We also show a characterization of the random walk data in terms of the corresponding transition matrices P , which highlights a new surprising nonlocal property. This work is motivated by the recent strong interest in the study of the fractional Calderón problem in the Riemannian setting.