  In this paper, we use machine learning to show that the Cheeger constant of a
connected regular graph has a predominant linear dependence on the largest two
eigenvalues of the graph spectrum. We also show that a trained deep neural
network on graphs of smaller sizes can be used as an effective estimator in
estimating the Cheeger constant of larger graphs.
