  The $k$-core decomposition is a widely studied summary statistic that
describes a graph's global connectivity structure. In this paper, we move
beyond using $k$-core decomposition as a tool to summarize a graph and propose
using $k$-core decomposition as a tool to model random graphs. We propose using
the shell distribution vector, a way of summarizing the decomposition, as a
sufficient statistic for a family of exponential random graph models. We study
the properties and behavior of the model family, implement a Markov chain Monte
Carlo algorithm for simulating graphs from the model, implement a direct
sampler from the set of graphs with a given shell distribution, and explore the
sampling distributions of some of the commonly used complementary statistics as
good candidates for heuristic model fitting. These algorithms provide first
fundamental steps necessary for solving the following problems: parameter
estimation in this ERGM, extending the model to its Bayesian relative, and
developing a rigorous methodology for testing goodness of fit of the model and
model selection. The methods are applied to a synthetic network as well as the
well-known Sampson monks dataset.
