Exponential-family Random Graph Models (ERGMs) constitute a large statistical framework
for modeling dense and sparse random graphs with short-or long-tailed degree distributions …

In this work, we describe a method that determines an exact map from a finite set of
subgraph densities to the parameters of a stochastic block model (SBM) matching these …

Generating graphs that preserve characteristic structures while promoting sample diversity
can be challenging, especially when the number of graph observations is small. Here, we …

We begin with a graph (or a directed graph), a single set of nodes N, and a set of lines or
arcs L. It is common to use this mathematical concept to represent a network as well as the …

An important challenge in the field of exponential random graphs (ERGs) is the fitting of non-
trivial ERGs on large graphs. By utilizing fast matrix block-approximation techniques, we …

We consider incomplete observations of stochastic processes governing the spread of
infectious diseases through finite populations by way of contact. We propose a flexible …

As technology advances, the manner in which humans communicate and collaborate
becomes increasingly intricate and the study of complex networks becomes ever important …

In this work, we describe a method that determines an exact map from a finite set of
subgraph densities to the parameters of a stochastic block model (SBM) matching these …

We present an elegant and flexible algorithm that provides the parameters of the simplest
stochastic block model (SBM) for a given set of prescribed subgraph densities, from which …