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 …
Random graphs, where the connections between nodes are considered random variables, have wide applicability in the social sciences. Exponential-family Random Graph Models (ERGM) have shown themselves to be a useful class of models for representing …
latentnet is a package to fit and evaluate statistical latent position and cluster models for networks. Hoff, Raftery, and Handcock (2002) suggested an approach to modeling networks based on positing the existence of an latent space of …
The ability to simulate graphs with given properties is important for the analysis of social networks. Sequential importance sampling has been shown to be particularly effective in estimating the number of graphs adhering to fixed marginals and in …