  The computational demands of community detection algorithms such as Louvain
and spectral optimization can be prohibitive for large networks. Eigenvector
centrality and Katz centrality are two network statistics commonly used to
describe the relative importance of nodes; and their calculation can be closely
approximated on large networks by scalable iterative methods. In this paper, we
present and leverage a surprising relationship between Katz centrality and
eigenvector centrality to detect communities. Beyond the computational gains,
we demonstrate that our approach identifies communities that are as good or
better than conventional methods.
