  We use a Potts model community detection algorithm to accurately and
quantitatively evaluate the hierarchical or multiresolution structure of a
graph. Our multiresolution algorithm calculates correlations among multiple
copies ("replicas") of the same graph over a range of resolutions. Significant
multiresolution structures are identified by strongly correlated replicas. The
average normalized mutual information, the variation of information, and other
measures in principle give a quantitative estimate of the "best" resolutions
and indicate the relative strength of the structures in the graph. Because the
method is based on information comparisons, it can in principle be used with
any community detection model that can examine multiple resolutions. Our
approach may be extended to other optimization problems. As a local measure,
our Potts model avoids the "resolution limit" that affects other popular
models. With this model, our community detection algorithm has an accuracy that
ranks among the best of currently available methods. Using it, we can examine
graphs over 40 million nodes and more than one billion edges. We further report
that the multiresolution variant of our algorithm can solve systems of at least
200000 nodes and 10 million edges on a single processor with exceptionally high
accuracy. For typical cases, we find a super-linear scaling, O(L^{1.3}) for
community detection and O(L^{1.3} log N) for the multiresolution algorithm
where L is the number of edges and N is the number of nodes in the system.
