network-modeling

We develop a method for improved estimation of a participant’s inclusion probability based on their network size (degree) as well as other information.

Claiming causal inferences in network settings necessitates careful consideration of the often complex dependency between outcomes for actors. Of particular importance are treatment spillover or outcome interference effects. We consider causal …

We show that smartphone based Exposure Notification systems can significantly improve the accuracy of short-term forecasting of COVID-19 caseloads.

We argue that a new class of exponential-family models for networks is more appriopriate when some of the covariates are stochastic.

We discuss network modeling with a novel exponential-family class of models when the network has some stochastic covariates and is only partially observed.

Many demographic problems require models for partnership formation. We consider a model for matchings within a bipartite population where individuals have utility for people based on observed and unobserved characteristics. It represents both the …

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 …

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 …

This is an invited discussion of `Nonparametric Bayes Modeling of Populations of Networks` by Daniele Durante, David B. Dunson and Joshua T. Vogelstein.

Gibbs measures are a fundamental class of distributions for the analysis of high dimensional data. Phase transitions, which are also known as degeneracy in the network science literature, are an emergent property of these models that well describe …