Community discovery is an important task for revealing structures in large networks. The massive size of contemporary social networks poses a tremendous challenge to the scalability of traditional graph clustering algorithms and the evaluation of discovered communities. We propose a divide-and-conquer strategy to discover hierarchical community structure, nonoverlapping within each level. Our algorithm is based on the highly efficient rank-2 symmetric nonnegative matrix factorization. We solve several implementation challenges to boost its efficiency on modern computer architectures, specifically for very sparse adjacency matrices that represent a wide range of social networks. 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.

中文翻译：

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通过 rank-2 对称非负矩阵分解进行分层社区检测。

社区发现是揭示大型网络结构的一项重要任务。当代社交网络的庞大规模对传统图聚类算法的可扩展性和已发现社区的评估提出了巨大挑战。我们提出了一种分而治之的策略来发现分层社区结构，每个级别内不重叠。我们的算法基于高效的 rank-2 对称非负矩阵分解。我们解决了几个实现挑战，以提高其在现代计算机架构上的效率，特别是对于代表广泛社交网络的非常稀疏的邻接矩阵。经验结果表明，我们的算法在最小化平均归一化切割方面具有竞争力的整体效率和领先的性能，并且我们的算法发现的非重叠社区比用于重叠社区检测的最新算法更好地恢复了真实社区。此外，我们提出了 DBLP 计算机科学书目网络的新数据集，该数据集具有更丰富的元数据和可验证的真实知识，可以促进未来在大型网络中社区发现和解释的研究。