The estimation of probabilities of network edges from the observed adjacency matrix has
important applications to the prediction of missing links and to network denoising. It is …

This paper studies the problem of estimating the graphon function–a generative mechanism
for a class of random graphs that are useful approximations to real networks. Specifically, a …

The supplementary information contains a self-contained development of the mathematical
theory of unbounded graphons over arbitrary probability spaces (Appendix A), a treatment of …

Consider the twin problems of estimating the connection probability matrix of an
inhomogeneous random graph and the graphon of a W-random graph. We establish the …

We consider local linear estimation of the graphon function, which determines probabilities
of pairwise edges between nodes in an unlabelled network. Real-world networks are …

This text on the theory and applications of network science is aimed at beginning graduate
students in statistics, data science, computer science, machine learning, and mathematics …

We study nonparametric methods for the setting where multiple distinct networks are
observed on the same set of nodes. Such samples may arise in the form of replicated …

Networks serve as a tool used to examine the large-scale connectivity patterns in complex
systems. Modelling their generative mechanism nonparametrically is often based on step …

This work addresses the rising demand for novel tools in statistical and machine learning
for" graph-valued random variables" by proposing a fast algorithm to compute the sample …

Network data can be modeled as an exchangeable graph model (ExGM), and graphon is a
two-dimensional function that generates an ExGM. The problem of graphon estimation has …