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 …
In many situations information from a sample of individuals can be supplemented by population level information on the relationship between a dependent variable and explanatory variables. Inclusion of the population level information can reduce bias …
We present a systematic examination of a real network data set using maximum likelihood estimation for exponential random graph models as well as new procedures to evaluate how well the models fit the observed networks. These procedures compare …
Objective: We present a likelihood based statistical framework to test the fit of power-law and alternative social process models for the degree distribution, and derive the sexually transmitted infection epidemic predictions from each model. Study …
We present a systematic examination of real network datasets using maximum likelihood estimation for exponential random graph models as well as new procedures to evaluate how well the models fit the observed graphs. These procedures compare …