Neural node embedding has been recently developed as a powerful representation for supervised tasks with graph data. We leverage this recent advance and propose a novel approach for unsupervised community discovery in graphs. Through extensive experimental studies on simulated and real-world data, we demonstrate consistent improvement of the proposed approach over the current state-of-the-arts. Specifically, our approach empirically attains the information theoretic limits under the benchmark Stochastic Block Models and exhibits better stability and accuracy over the best known algorithms in the community recovery limits.