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Spectral partitioning in random regular blockmodels

Graph partitioning problems emerge in a wide variety of complex systems, ranging from biology to finance, but can be rigorously analyzed and solved only for a few graph ensembles. Here, an ensemble of random graphs with regular block structure is introduced, for which analytical results can be obtained. In particular, the spectral density of such random regular blockmodels is computed exactly for a modular, bipartite and core-periphery structure. McKay’s law for random regular graphs is found analytically to apply also for regular modular and bipartite structures when blocks are homogeneous. In core-periphery structures, where blocks are intrinsically heterogeneous, a new law is found to apply for the spectral density. Exact solution to the inference problem is provided for the models discussed. All analytical results show perfect agreement with numerical experiments. Final discussion summarizes results and outlines the relevance of the results for the solution of graph partitioning problems in other graph ensembles, in particular for the study of detectability thresholds and resolution limits.