We argue that a new class of exponential-family models for networks is more appriopriate when some of the covariates are stochastic.
In this talk we discuss a new class of models for social networks that which circumvents the issue of near-degeneracy while maintaining the desirable features of exponential-family models.
In this talk we discuss a new class of models for social networks that which circumvents the issue of near-degeneracy while maintaining the desirable features of exponential-family models.
Exponential-family Random Graph Models (ERGMs) have long been at the forefront of the analysis of relational data. The exponential-family form allows complex network dependencies to be represented. Models in this class are interpretable, flexible and …
In this talk we assess the real-world performance of Latent Order Logistic models (LOLOG).
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). We demonstrate that the LOLOG models are, in …