  In this paper we introduce a new method to locate highly connected clusters
in a network. Our proposed approach adapts the HyperBall algorithm to localize
regions with a high density of small subgraph patterns in large graphs in a
memory-efficient manner. We use this method to evaluate three measures of
subgraph connectivity: conductance, the number of triangles, and transitivity.
We demonstrate that our algorithm, applied to these measures, helps to identify
clustered regions in graphs, and provides good seed sets for community
detection algorithms such as PageRank-Nibble. We analytically obtain the
performance guarantees of our new algorithms, and demonstrate their
effectiveness in a series of numerical experiments on synthetic and real-world
networks.
