  Exchangeable graphs arise via a sampling procedure from measurable functions
known as graphons. A natural estimation problem is how well we can recover a
graphon given a single graph sampled from it. One general framework for
estimating a graphon uses step-functions obtained by partitioning the nodes of
the graph according to some clustering algorithm. We propose an iterative
step-function estimator (ISFE) that, given an initial partition, iteratively
clusters nodes based on their edge densities with respect to the previous
iteration's partition. We analyze ISFE and demonstrate its performance in
comparison with other graphon estimation techniques.
