Title: Exponential random graphs behave like mixtures of stochastic block models,
Abstract: We study the behavior of exponential random graphs in both the sparse and the
dense regime. We show that exponential random graphs are approximate mixtures
of graphs with independent edges whose probability matrices are critical points
of an associated functional, thereby satisfying a certain matrix equation. In
the dense regime, every solution to this equation is close to a block matrix,
concluding that the exponential random graph behaves roughly like a mixture of
stochastic block models. We also show existence and uniqueness of solutions to
this equation for several families of exponential random graphs, including the
case where the subgraphs are counted with positive weights and the case where
all weights are small in absolute value. In particular, this generalizes some
of the results in a paper by Chatterjee and Diaconis from the dense regime to
the sparse regime and strengthens their bounds from the cut-metric to the
one-metric.