The analysis of network data is an area that is rapidly growing, both within and outside of the
discipline of statistics. This review provides a concise summary of methods and models used …

Models with intractable normalizing functions arise frequently in statistics. Common
examples of such models include exponential random graph models for social networks and …

Closed-form, interpretable mathematical models have been instrumental for advancing our
understanding of the world; with the data revolution, we may now be in a position to uncover …

This paper proposes an empirical model of network formation, combining strategic and
random networks features. Payoffs depend on direct links, but also link externalities. Players …

This paper analyzes the endogeneity bias problem caused by associations of members
within a network when the spatial autoregressive (SAR) model is used to study social …

As the prevalence of data analysis grows, safeguarding data privacy has become a
paramount concern. Consequently, there has been an upsurge in the development of …

This unique textbook provides an introduction to statistical inference with network data. The
authors present a self-contained derivation and mathematical formulation of methods …

Monte Carlo algorithms often aim to draw from a distribution π π by simulating a Markov
chain with transition kernel PP such that π π is invariant under P P. However, there are many …

A large number of statistical models are “doubly-intractable”: the likelihood normalising term,
which is a function of the model parameters, is intractable, as well as the marginal likelihood …

Exponential-family Random Graph Models (ERGMs) constitute a large statistical framework
for modeling dense and sparse random graphs with short-or long-tailed degree distributions …