Graphex processes resolve some pathologies in traditional random graph models, notably, providing models that are both projective and allow sparsity. Most of the literature on graphex processes study them from a probabilistic point of view. Techniques for inferring the parameter of these processes – the so-called graphon – are still marginal; exceptions are a few papers considering parametric families of graphons. Nonparametric estimation remains unconsidered. In this paper, we propose estimators for a selected choice of functionals of the graphon. Our estimators originate from the subsampling theory for graphex processes, hence can be seen as a form of bootstrap procedure.
"Bootstrap estimators for the tail-index and for the count statistics of graphex processes." Electron. J. Statist. 15 (1) 282 - 325, 2021. https://doi.org/10.1214/20-EJS1789