Adjusting for network size and composition effects in exponential family random graph models

Authors

Pavel N. Krivitsky, University of WollongongFollow
M Handcock, University of California at Los Angeles
Martina Morris, University of Washington

RIS ID

92721

Publication Details

Krivitsky, P. N.., Handcock, M. & Morris, M. (2011). Adjusting for network size and composition effects in exponential family random graph models. Statistical Methodology, 8 (4), 319-339.

Abstract

Exponential-family random graph models (ERGMs) provide a principled way to model and simulate features common in human social networks, such as propensities for homophily and friend-ofa- friend triad closure. We show that, without adjustment, ERGMs preserve density as network size increases. Density invariance is often not appropriate for social networks. We suggest a simple modification based on an offset which instead preserves the mean degree and accommodates changes in network composition asymptotically. We demonstrate that this approach allows ERGMs to be applied to the important situation of egocentrically sampled data. We analyze data from the National Health and Social Life Survey (NHSLS).