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Social Network Community Detection Using Strongly Connected Components

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Trends and Applications in Knowledge Discovery and Data Mining (PAKDD 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8643))

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Abstract

The hasty information growth of social network poses the information searching efficiency trials for network mining research. Social network graphs and web graphs are huge sources of highly densely connected hypertext links so that the social networks can be described by a directed graph. This kind of network has inherent structural characteristics such as overly expanded, duplicated, connectedness, and circuit paths, which could generate serious challenges for structured searching for sub-network isomorphism and community detection. In this paper, an efficient searching algorithm is suggested to discover social network communities for overcoming the circuit path issue embedded in the social network environment. Experimental results indicate that the proposed algorithm has better performance than the traditional circuit searching algorithms in terms of the time complexity as well as performance criteria.

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Acknowledgement

This work was supported by Defense Acquisition Program Administration and Agency for Defense Development under the Contract UD080042AD, Korea.

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Authors and Affiliations

  1. Informatics Laboratory, Department of I.E, INHA University, 253 Yong-Hyun Dong, Incheon, 402-751, South Korea

    Wookey Lee & James J. Lee

  2. Department of Computer Science, Kangwon National University, 192-1 Hyoja-Dong, Chuncheon, Kangwon, 200-701, South Korea

    Jinho Kim

Authors
  1. Wookey Lee
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  2. James J. Lee
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  3. Jinho Kim
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Corresponding author

Correspondence to Jinho Kim .

Editor information

Editors and Affiliations

  1. National Chiao Tung University, Hsinchu, Taiwan

    Wen-Chih Peng

  2. Google Research, Mountain View, California, USA

    Haixun Wang

  3. University of Melbourne, Melbourne, Victoria, Australia

    James Bailey

  4. National Cheng Kung University, Tainan, Taiwan

    Vincent S. Tseng

  5. Japan Advanced Institute of Science and Technology, Nomi City, Japan

    Tu Bao Ho

  6. Nanjing University, Nanjing, China

    Zhi-Hua Zhou

  7. National Chengchi University, Taipei, Taiwan

    Arbee L.P. Chen

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© 2014 Springer International Publishing Switzerland

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Lee, W., Lee, J.J., Kim, J. (2014). Social Network Community Detection Using Strongly Connected Components. In: Peng, WC., et al. Trends and Applications in Knowledge Discovery and Data Mining. PAKDD 2014. Lecture Notes in Computer Science(), vol 8643. Springer, Cham. https://doi.org/10.1007/978-3-319-13186-3_53

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  • DOI: https://doi.org/10.1007/978-3-319-13186-3_53

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-13185-6

  • Online ISBN: 978-3-319-13186-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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