Probing the geometry of data with diffusion Fréchet functions

The state of many complex systems, such as ecosystems formed by multiple microbial taxa that interact in intricate ways, is often summarized as a probability distribution on the nodes of a weighted network. This paper develops methods for modeling the organization of such data, as well as their Euclidean counterparts, across spatial scales. Using the notion of diffusion distance, we introduce diffusion Frechet functions and diffusion Frechet vectors associated with probability distributions on Euclidean space and the vertex set of a weighted network, respectively. We prove that these functional statistics are stable with respect to the Wasserstein distance between probability measures, thus yielding robust descriptors of their shapes. We provide several examples that illustrate the geometric characteristics of a distribution that are captured by multi-scale Frechet functions and vectors.