Neural node embeddings have recently emerged as a powerful representation forsupervised learning tasks involving graph-structured data. We leverage thisrecent advance to develop a novel algorithm for unsupervised communitydiscovery in graphs. Through extensive experimental studies on simulated andreal-world data, we demonstrate that the proposed approach consistentlyimproves over the current state-of-the-art. Specifically, our approachempirically attains the information-theoretic limits for community recoveryunder the benchmark Stochastic Block Models for graph generation and exhibitsbetter stability and accuracy over both Spectral Clustering and Acyclic BeliefPropagation in the community recovery limits.
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