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SIMPLE: a simplifying-ensembling framework for parallel community detection from large networks

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Abstract

Community detection is a classic and very difficult task in complex network analysis. As the increasingly explosion of social media, scaling community detection methods to large networks has attracted considerable recent interests. In this paper, we propose a novel SIMPLifying and Ensembling (SIMPLE) framework for parallel community detection. It employs the random link sampling to simplify the network and obtain basic partitionings on every sampled graphs. Then, the K-means-based Consensus Clustering is used to ensemble a number of basic partitionings to get high-quality community structures. All of phases in SIMPLE, including random sampling, sampled graph partitioning, and consensus clustering, are encapsulated into MapReduce for parallel execution. Experiments on six real-world social networks analyze key parameters and factors inside SIMPLE, and demonstrate both effectiveness and efficiency of the SIMPLE.

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Acknowledgments

This research was partially supported by National Natural Science Foundation of China under Grants 71571093, 71372188 and 61502222, National Center for International Joint Research on E-Business Information Processing under Grant 2013B01035, National Key Technologies R&D Program of China under Grants 2013BAH16F01 and 2013BAH16F04, Industry Projects in Jiangsu S&T Pillar Program under Grant BE2014141, Natural Science Foundation of Jiangsu Province of China under Grant SBK2015042593, and Key/Surface Projects of Natural Science Research in Jiangsu Provincial Colleges and Universities under Grants 12KJA520001, 14KJA520001, 14KJB520015, 15KJB520012 and 15KJB520011.

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Authors and Affiliations

  1. Jiangsu Provincial Key Laboratory of E-Business, Nanjing University of Finance and Economics, Nanjing, China

    Zhiang Wu, Zhan Bu & Jie Cao

  2. College of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China

    Guangliang Gao

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  1. Zhiang Wu
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Correspondence to Zhan Bu.

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Wu, Z., Gao, G., Bu, Z. et al. SIMPLE: a simplifying-ensembling framework for parallel community detection from large networks . Cluster Comput 19, 211–221 (2016). https://doi.org/10.1007/s10586-015-0504-2

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