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An Overlapping Community Detection Algorithm Based on Triangle Coarsening and Dynamic Distance

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Computer Supported Cooperative Work and Social Computing (ChineseCSCW 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 917))

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Abstract

Discoverying hidden communities in various kinds of complicated networks is a considerable research direction in the field of complex network analysis. Its goal is to discover the structures of communities in complex networks. The algorithms devised upon the Attractor dynamic distance mechanism are capable of finding stable communities with various sizes. However, they still have deficiencies in overlapping community discovery and runtime efficiency. An overlapping community discovery algorithm based on triangle coarsening and dynamic distance is posed in this paper. First, a coarsening strategy devised upon triangle is adopted to reduce networks’ sizes. Second, for the coarsened networks, a dynamic distance processing mechanism based on overlapping Attractors is used to discover the overlapping communities in the networks. Finally, the communities in the raw networks are obtained through anti-roughening steps. The experiments on different datasets demonstrate that the proposed algorithm not only can discover the overlapping communities accurately but also has low time complexity.

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Acknowledgments

This work is partly supported by the National Natural Science Foundation of China under Grant No. 61300104, No. 61300103 and No. 61672158, the Fujian Province High School Science Fund for Distinguished Young Scholars under Grant No. JA12016, the Program for New Century Excellent Talents in Fujian Province University under Grant No. JA13021, the Fujian Natural Science Funds for Distinguished Young Scholar under Grant No. 2014J06017 and No. 2015J06014, the Major Production and Research Project of Fujian Scientific and Technical Department, the Technology Innovation Platform Project of Fujian Province (Grants Nos. 2009J1007, 2014H2005), the Fujian Collaborative Innovation Center for Big Data Applications in Governments, and the Natural Science Foundation of Fujian Province under Grant No. 2013J01230 and No. 2014J01232, Industry-Academy Cooperation Project under Grant No. 2014H6014 and No. 2017H6008. Haixi Government Big Data Application Cooperative Innovation Center.

Author information

Authors and Affiliations

  1. College of Mathematics and Computer Sciences, Fuzhou University, Fuzhou, 350116, China

    Bingjie Xiang, Kun Guo & Zhanghui Liu

  2. Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing, Fuzhou, China

    Bingjie Xiang, Kun Guo & Zhanghui Liu

  3. Key Laboratory of Spatial Data Mining and Information Sharing, Ministry of Education, Fuzhou, 350116, China

    Bingjie Xiang, Kun Guo & Zhanghui Liu

  4. Power Science and Technology Corporation State Grid Information and Telecommunication Group, Xiamen, 351008, China

    Qinwu Liao

Authors
  1. Bingjie Xiang
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  2. Kun Guo
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  3. Zhanghui Liu
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  4. Qinwu Liao
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Corresponding author

Correspondence to Zhanghui Liu .

Editor information

Editors and Affiliations

  1. Shandong University, Jinan, China

    Yuqing Sun

  2. Fudan University, Shanghai, China

    Tun Lu

  3. Guilin University of Technology, Guilin, China

    Xiaolan Xie

  4. University of Shanghai for Science and Technology, Shanghai , China

    Liping Gao

  5. Tongji University, Shanghai, China

    Hongfei Fan

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Xiang, B., Guo, K., Liu, Z., Liao, Q. (2019). An Overlapping Community Detection Algorithm Based on Triangle Coarsening and Dynamic Distance. In: Sun, Y., Lu, T., Xie, X., Gao, L., Fan, H. (eds) Computer Supported Cooperative Work and Social Computing. ChineseCSCW 2018. Communications in Computer and Information Science, vol 917. Springer, Singapore. https://doi.org/10.1007/978-981-13-3044-5_21

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  • DOI: https://doi.org/10.1007/978-981-13-3044-5_21

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-3043-8

  • Online ISBN: 978-981-13-3044-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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