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

Duncan A. Clark and Mark S. Handcock

Journal of the Royal Statistical Society Series A, 2022, vol. 185, issue 2, 566-587

Abstract: Exponential‐family random graph models (ERGMs) are widely used in social network analysis when modelling data on the relations between actors. ERGMs are typically interpreted as a snapshot of a network at a given point in time or in a final state. The recently proposed Latent Order Logistic model (LOLOG) directly allows for a latent network formation process. We assess the real‐world performance of these models when applied to typical networks modelled by researchers. Specifically, we model data from an ensemble of articles in the journal Social Networks with published ERGM fits, and compare the ERGM fit to a comparable LOLOG fit. 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. Our results support the general use of LOLOG models in circumstances where ERGMs are considered.

Date: 2022
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