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Community detection is a fundamental statistical problem in network dataanalysis. Many algorithms have been proposed to tackle this problem. Most ofthese algorithms are not guaranteed to achieve the statistical optimality ofthe problem, while procedures that achieve information theoretic limits forgeneral parameter spaces are not computationally tractable. In this paper, wepresent a computationally feasible two-stage method that achieves optimalstatistical performance in misclassification proportion for stochastic blockmodel under weak regularity conditions. Our two-stage procedure consists of ageneric refinement step that can take a wide range of weakly consistentcommunity detection procedures as initializer, to which the refinement stageapplies and outputs a community assignment achieving optimal misclassificationproportion with high probability. The practical effectiveness of the newalgorithm is demonstrated by competitive numerical results.

Achieving Optimal Misclassification Proportion in Stochastic Block Model