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International Conference on Web Information Systems and Applications

WISA 2020: Web Information Systems and Applications pp 415–427Cite as

An Evolutionary Algorithm Based on Compressed Representation for Computing Weak Structural Balance in Large-Scale Signed Networks

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12432))

Abstract

With the integration of sentimental information, signed networks have a wide range of applications. The calculation of the degree of weak unbalance, which reflects the tension between positive and negative relations, is an NP-hard problem. In this paper, an evolutionary algorithm EAWSB for computing the degree of weak unbalance is proposed, where an indirect individual representation based on compression is designed to reduce the space complexity of the algorithm. In addition, a rotation operator is proposed to increase the population diversity. Experimental results show the effectiveness and efficacy of EAWSB. A thorough comparison show that EAWSB outperforms or is comparable to other state-of-the-art algorithms.

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Author information

Authors and Affiliations

  1. Information College, Shanxi University of Finance and Economics, Taiyuan, China

    Xingong Chang & Fei Zhang

Authors
  1. Xingong Chang
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  2. Fei Zhang
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Corresponding author

Correspondence to Xingong Chang .

Editor information

Editors and Affiliations

  1. Guangzhou University, Guangzhou, China

    Guojun Wang

  2. The University of New South Wales, Sydney, NSW, Australia

    Xuemin Lin

  3. Rensselaer Polytechnic Institute, Troy, NY, USA

    James Hendler

  4. Wuhan University, Wuhan, China

    Wei Song

  5. Hohai University, Nanjing, China

    Zhuoming Xu

  6. Fuzhou University, Fuzhou, China

    Genggeng Liu

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Chang, X., Zhang, F. (2020). An Evolutionary Algorithm Based on Compressed Representation for Computing Weak Structural Balance in Large-Scale Signed Networks. In: Wang, G., Lin, X., Hendler, J., Song, W., Xu, Z., Liu, G. (eds) Web Information Systems and Applications. WISA 2020. Lecture Notes in Computer Science(), vol 12432. Springer, Cham. https://doi.org/10.1007/978-3-030-60029-7_38

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  • DOI: https://doi.org/10.1007/978-3-030-60029-7_38

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-60028-0

  • Online ISBN: 978-3-030-60029-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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