Empirical results have shown that our algorithm has competitive overall efficiency and leading performance in minimizing the average normalized cut, and that the nonoverlapping communities found by our algorithm recover the ground-truth communities better than state-of-the-art algorithms for overlapping community detection. In addition, we present a new dataset of the DBLP computer science bibliography network with richer meta-data and verifiable ground-truth knowledge, which can foster future research in community finding and interpretation of communities in large networks.