In this paper we present an estimation algorithm for Bayesian exponential random multi-
graphs (BERmGMs) under missing network data. Social actors are often connected with …

We extend the well-known and widely used exponential random graph model (ERGM) by
including nodal random effects to compensate for heterogeneity in the nodes of a network …

We consider partially observed network data as defined in Handcock and Gile (2010). More
specifically we introduce an elaboration of the Bayesian data augmentation scheme of …

Recent advances in computational methods for intractable models have made network data
increasingly amenable to statistical analysis. Exponential random graph models (ERGMs) …

Social network analysis has typically concerned analysis of one type of tie connecting nodes
of the same type. It has however been recognised that people are connected through …

Models with intractable likelihood functions arise in areas including network analysis and
spatial statistics, especially those involving Gibbs random fields. Posterior parameter …

In this paper we describe the main featuress of the Bergm package for the open-source R
software which provides a comprehensive framework for Bayesian analysis for exponential …

Missing data are often problematic in social network analysis since what is missing may
potentially alter the conclusions about what we have observed as tie-variables need to be …

Exchangeable graph models (ExGM) are a nonparametric approach to modeling network
data that subsumes a number of popular models. The key object that defines an ExGM is …

In most domains of network analysis researchers consider networks that arise in nature with
weighted edges. Such networks are routinely dichotomized in the interest of using available …