Abstract: 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 ...

The collection of data on populations of networks is becoming increasingly common, where each data point can be seen as a realisation of a network-valued random variable. Moreover, each data point may ...

The growing availability of network data and of scientific interest in distributed systems has led to the rapid development of statistical models of network structure. Typically, however, these are ...

Venture capital firms use a variety of accumulated resources to inform their investment activities, but do the rely solely on their own resources or do they employ other firms' resources to complement ...

We explore the asymptotic properties of strategic models of network formation in very large populations. Specifically, we focus on (undirected) exponential random graph models. We want to recover a ...

Abstract: A variety of random graph models have been developed in recent years to study a range of problems on networks, driven by the wide availability of data from many social, telecommunication, ...

This code implements a Bayesian estimation approach for exponential random graph models (ERGMs) using a double Metropolis-Hasting (DMH) algorithm. ERGMs are statistical models used for modeling social ...

In pg_ergm_eest.py you will find an implementation of the main algorithms described in the paper. The EEsparse algorithm is the revised Equilibrium Expectation algorithm used to estimate the ...