Exponential-family models for dependent data have applications in a wide variety of areas,
but the dependence often results in an intractable likelihood, requiring either analytic …

This workshop will provide an introductory tutorial on exponential-family random graph
models (ERGMs) for social networks, emphasizing a hands-on approach to fitting these …

One impediment to the statistical analysis of network data has been the difficulty in modeling
the dependence among the observations. In the very simple case of binary (0-1) network …

We study the problem of network regression, where one is interested in how the topology of
a network changes as a function of Euclidean covariates. We build upon recent …

Network data arise in a wide variety of applications. Although descriptive statistics for
networks abound in the literature, the science of fitting statistical models to complex network …

Abstract Exponential Random Graphs Models (ERGM) are common, simple statistical
models for social network and other network structures. Unfortunately, inference and …

Full probability models are critical for the statistical modeling of complex networks, and yet
there are few general, flexible and widely applicable generative methods. We propose a …

The development of models and methodology for the analysis of data from multiple
heterogeneous networks is of importance both in statistical network theory and across a …

Abstract Markov Random Fields, or undirected graphical models are widely used to model
high-dimensional multivariate data. Classical instances of these models, such as Gaussian …

Random hypergraph is a broad concept used to describe probability distributions over
hypergraphs, which are mathematical structures with applications in various fields, eg …