This repository contains the code and datasets used for the two models we have built so far. The first model uses an autoregressive normalizing flow to learn an invertible mapping between the embedded network space and a latent probability space. The second model uses a neural network to learn the parameters of a  probability distribution that aims to capture the behavior of the neighborhood embeddings.