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Social Network Clustering by Using Genetic Algorithm: A Case Study

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Trends in Applied Knowledge-Based Systems and Data Science (IEA/AIE 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9799))

Abstract

With the rapid growth of large-scaled social networks, the analysis of social network data has become an extremely challenging computational issue. To meet the challenge, it is possible to significantly reduce the complexity of the problem by properly clustering a large social network into groups, and then analyzing data within each group, or studying the relationship among groups. Hence, social network clustering can be regarded as one of the essential problems in social network analysis. To address the issue, we propose an evolutionary computation approach to social network clustering. We first formulate social network clustering as an optimization problem and then develop a genetic algorithm to solve the problem. We also applied the proposed approach to a case study based on data of some Facebook users.

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Notes

  1. 1.

    Note that the properties of individuals do not play any role in the connection-based criterion, although it is possible to combine both connection-based and similarity-based criteria by taking such features into consideration.

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Acknowledgment

This work was partially supported by the Ministry of Science and Technology of Taiwan under Grants MOST 103-2410-H-346-007-MY2 and MOST 104-2221-E-001-010-MY3.

Author information

Authors and Affiliations

  1. Department of Computer Science and Information Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung, 807, Taiwan

    Ming-Feng Tsai

  2. Department of Information Management, National Penghu University of Science and Technology, Penghu, 880, Taiwan

    Chun-Yi Lu

  3. Institute of Information Science Academia Sinica, Taipei, 115, Taiwan

    Churn-Jung Liau

  4. Department of Computer Science and Information Engineering, National Penghu University of Science and Technology, Penghu, 880, Taiwan

    Tuan-Fang Fan

Authors
  1. Ming-Feng Tsai
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  2. Chun-Yi Lu
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  3. Churn-Jung Liau
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  4. Tuan-Fang Fan
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Corresponding author

Correspondence to Tuan-Fang Fan .

Editor information

Editors and Affiliations

  1. Iwate Prefectural University , Iwate, Japan

    Hamido Fujita

  2. Department Computer Science, Texas State University, San Marcos, Texas, USA

    Moonis Ali

  3. Universiti Teknologi Malaysis (UTM), Bahru, Malaysia

    Ali Selamat

  4. Iwate Prefectural University , Iwate, Japan

    Jun Sasaki

  5. Iwate Prefectural University , Iwate, Japan

    Masaki Kurematsu

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© 2016 Springer International Publishing Switzerland

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Tsai, MF., Lu, CY., Liau, CJ., Fan, TF. (2016). Social Network Clustering by Using Genetic Algorithm: A Case Study. In: Fujita, H., Ali, M., Selamat, A., Sasaki, J., Kurematsu, M. (eds) Trends in Applied Knowledge-Based Systems and Data Science. IEA/AIE 2016. Lecture Notes in Computer Science(), vol 9799. Springer, Cham. https://doi.org/10.1007/978-3-319-42007-3_25

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  • DOI: https://doi.org/10.1007/978-3-319-42007-3_25

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-42006-6

  • Online ISBN: 978-3-319-42007-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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