Snowball sampling for estimating exponential random graph models for large networks

Snowball sampling for estimating exponential random graph models for large networks

The exponential random graph model (ERGM) is a well-established statistical approach to modelling social network data. However, Monte Carlo estimation of ERGM parameters is a computationally intensive procedure that imposes severe limits on the size of full networks that can be fitted. We demonstrate the use of snowball sampling and conditional estimation to estimate ERGM parameters for large n...

Exponential random graph models for multilevel networks

Modern multilevel analysis, whereby outcomes of individuals within groups take into account group membership, has been accompanied by impressive theoretical development (e.g. Kozlowski and Klein, 2000) and sophisticated methodology (e.g. Snijders and Bosker, 2012). But typically the approach assumes that links between groups are non-existent, and interdependence among the individuals derives so...

Exponential random graph models for affiliation networks

Statistical modeling of social networks as complex systems has always been and remains a challenge for social scientists. Exponential family models give us a convenient way of expressing local network structures that have sufficient statistics for their corresponding parameters. This kind of model, known as Exponential Random Graph Models (ERGMs), or p∗ models, have been developed since the 198...

Conditional estimation of exponential random graph models from snowball sampling designs

A complete survey of a network in a large population may be prohibitively difficult and costly. So it is important to estimate models for networks using data from various network sampling designs, such as link-tracing designs. We focus here on snowball sampling designs, designs in which the members of an initial sample of network members are asked to nominate their network partners, their netwo...

A Perfect Sampling Method for Exponential Random Graph Models