  Stochastic Kronecker graphs supply a parsimonious model for large sparse real
world graphs. They can specify the distribution of a large random graph using
only three or four parameters. Those parameters have however proved difficult
to choose in specific applications. This article looks at method of moments
estimators that are computationally much simpler than maximum likelihood. The
estimators are fast and in our examples, they typically yield Kronecker
parameters with expected feature counts closer to a given graph than we get
from KronFit. The improvement was especially prominent for the number of
triangles in the graph.
