Comparing the real-world performance of exponential-family random graph models and latent order logistic models for social network analysis

Abstract

We assess the real-world performance of Latent Order Logistic models (LOLOG) when applied to typical networks modelled by researchers by comparing them to Exponential-family random graph models (ERGMs) over an ensemble of published networks. We demonstrate that the LOLOG models are, in general, in qualitative agreement with the ERGM models, and provide at least as good a model fit. In addition, they are typically faster and easier to fit to data, without the tendency for degeneracy that plagues ERGMs.

This is joint work with Duncan A. Clark.