Among popular models, exponential random graph models (ERGM) have been developed to study these complex networks. For large networks, however, maximum likelihood estimation (MLE) of parameters in ...

Michael Schweinberger, Mark S. Handcock, Local dependence in random graph models: characterization, properties and statistical inference, Journal of the Royal Statistical Society.

We study the asymptotics for sparse exponential random graph models where the parameters may depend on the number of vertices of the graph. We obtain exact estimates for the mean and variance of the ...

With the growth of interest in network data across fields, the Exponential Random Graph Model (ERGM) has emerged as the leading approach to the statistical analysis of network data. ERGM parameter ...

Given these backgrounds, applying the exponential random graph model to unique investment data of Japanese VC firms over the last two decades, we empirically examine the relationship between VC firm ...

We define a general class of network formation models, Statistical Exponential Random Graph Models (SERGMs), that nest standard exponential random graph models (ERGMs) as a special case. We provide ...

In order to specify how firms choose their partners, the so-called exponential random graph model is applied to estimate the ties formation process. For the estimation of such a large-scale network, ...

An exponential random graph model (ergm) is a probability distribution over graphs specified by a set of sufficient statistics and and corresponding parameters.