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International Conference on Intelligent Data Engineering and Automated Learning

IDEAL 2017: Intelligent Data Engineering and Automated Learning – IDEAL 2017 pp 9–19Cite as

Dynamic Community Detection Algorithm Based on Automatic Parameter Adjustment

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  • 2017 Accesses

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10585))

Abstract

Community detection is widely used in social network analysis. It clusters densely connected vertices into communities. As social networks get larger, scalable algorithms are drawing more attention. Among those methods, the algorithm named Attractor is quite outstanding both in terms of accuracy and scalability. However, it is highly dependent on the parameter, which is abstract for users. The improper parameter value can bring about some problems. There can be a huge community (monster) sometimes; other time the communities are generally too small (fragments). The existing fragments also need eliminating. Such phenomenon greatly deteriorates the performance of Attractor. We modify the algorithm and propose mAttractor, which adjusts the parameter automatically. We introduce two constraints to limit monsters and fragments and to narrow the parameter range. An optional parameter is also introduced. The proposed algorithm can choose to satisfy or ignore the optional parameter by judging whether it is reasonable. Our algorithm also eliminates the existing fragments. A delicate pruning is designed for fast determination. Experiments show that our mAttractor outperforms Attractor by 2%–270%.

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Acknowledgement

This work is partially supported by The National Key Research and Development Program of China (2016YFB0200401), by program for New Century Excellent Talents in University, by National Science Foundation (NSF) China 61402492, 61402486, 61379146, by the laboratory pre-research fund (9140C810106150C81001).

Author information

Authors and Affiliations

  1. College of Computer Science, National University of Defense Technology, Changsha, People’s Republic of China

    Kai Lu, Xin Wang & Xiaoping Wang

  2. Science and Technology on Parallel and Distributed Processing Laboratory, National University of Defense Technology, Changsha, People’s Republic of China

    Kai Lu, Xin Wang & Xiaoping Wang

Authors
  1. Kai Lu
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  2. Xin Wang
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  3. Xiaoping Wang
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Corresponding author

Correspondence to Xin Wang .

Editor information

Editors and Affiliations

  1. University of Manchester, Manchester, United Kingdom

    Hujun Yin

  2. School of Electronic and Electrical Engineering, Nanjing University, Nanjiing, China

    Yang Gao

  3. Nanjing University of Aeronautics and Astronautics, Nanjing, China

    Songcan Chen

  4. Guilin University of Electronic Technology, Guilin, China

    Yimin Wen

  5. Guilin University of Electronic Technology, Guilin, China

    Guoyong Cai

  6. Guilin University of Electronic Technology, Guilin, China

    Tianlong Gu

  7. Beijing University of Posts and Telecommunications, Beijing, China

    Junping Du

  8. University of Seville, Seville, Spain

    Antonio J. Tallón-Ballesteros

  9. Southeast University, Nanjing, China

    Minling Zhang

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© 2017 Springer International Publishing AG

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Lu, K., Wang, X., Wang, X. (2017). Dynamic Community Detection Algorithm Based on Automatic Parameter Adjustment. In: Yin, H., et al. Intelligent Data Engineering and Automated Learning – IDEAL 2017. IDEAL 2017. Lecture Notes in Computer Science(), vol 10585. Springer, Cham. https://doi.org/10.1007/978-3-319-68935-7_2

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  • DOI: https://doi.org/10.1007/978-3-319-68935-7_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-68934-0

  • Online ISBN: 978-3-319-68935-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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