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 …

The collection of data on populations of networks is becoming increasingly common, where
each data point can be seen as a realisation of a network-valued random variable. A …

The Bergm package provides a comprehensive framework for Bayesian inference using
Markov chain Monte Carlo (MCMC) algorithms. It can also supply graphical Bayesian …

I congratulate the authors on their timely and insightful article. Since the advent of network
analysis, there has been the question of the meaning of sample size in a network setting …

Probabilistic Foundations of Statistical Network Analysis presents a fresh and insightful
perspective on the fundamental tenets and major challenges of modern network analysis. Its …

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 …

Bayesian inference for exponential family random graph models (ERGMs) is a doubly-
intractable problem because of the intractability of both the likelihood and posterior …

Statistical analysis of social networks provides valuable insights into complex network
interactions across various scientific disciplines. However, accurate modeling of networks …

Exponential-family random graph models (ERGMs) are probabilistic network models that are
parametrized by sufficient statistics based on structural (ie, graph-theoretic) properties. The …