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The computational demands of community detection algorithms such as Louvainand spectral optimization can be prohibitive for large networks. Eigenvectorcentrality and Katz centrality are two network statistics commonly used todescribe the relative importance of nodes; and their calculation can be closelyapproximated on large networks by scalable iterative methods. In this paper, wepresent and leverage a surprising relationship between Katz centrality andeigenvector centrality to detect communities. Beyond the computational gains,we demonstrate that our approach identifies communities that are as good orbetter than conventional methods.

Community Detection via Katz and Eigenvector Centrality