Overlapping Community Detection with a Maximal Clique Enumeration Method in MapReduce

Su, Yi-Jen; Hsu, Wei-Lin; Wun, Jian-Cheng

doi:10.1007/978-3-319-07776-5_38

Cited by 2 publications

(1 citation statement)

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“…MapReduce 实现所有操作的充分并行化, 从而大大提高了传统 GN 方法的运算性能. Ovelgönne [20] 则实现了一种可并行的基于标签传播的社区挖掘方法, 可以基于 MapReduce 实现并运行于 Hadoop 集群计算平台, 具有高度可扩展性; 此外, Su 等 [21] 提出了一种基于 MapReduce 实现的最大派系枚举方法, 可以用于挖掘社交网络中的重叠社区并显著提升挖掘效率. 对以上相关工作分析可知, NMF 适合用于挖掘复杂网络的社区以及 MapReduce 可以基于分布式集群计算平台解决大规模社区挖掘问题.…”

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Large-scale topic community mining based on distributed nonnegative matrix factorization

Tang

Yang

et al. 2016

Sci. Sin.-Inf.

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Mining topic community in complex networks is of great applicable value. However, most of the existing methods cannot effectively mine topic community in large-scale complex networks because of their weak scalabilities. To rectify this problem, we propose a method called TCMDNMF that is based on the joint nonnegative matrix factorization model. The proposed method can effectively integrate node link and content information to mine topic community. We adopt the gradient descent method as the optimized solution to the topic community mining model. Further, to improve the computing efficiency of TCMDNMF, we use L1 norm as the sparsity regularization term and implement the key algorithms based on the MapReduce distributed computing framework. The results of extensive experiments conducted demonstrate that our method is effective and is highly scalable. Furthermore, it very effectively solves the problem of processing large volumes of data brought by topic community mining in large-scale complex networks.

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Section: 相关工作unclassified