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Network Structure Balance Analytics with Evolutionary Optimization

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Computational Intelligence for Network Structure Analytics

Abstract

Structural balance enables a comprehensive understanding of the potential tensions and conflicts beneath signed networks, and its computation and transformation have attracted increasing attention in recent years. The balance computation aims at evaluating the distance from an unbalanced network to a balanced one, and the balance transformation is to convert an unbalanced network into a balanced one. This chapter focuses on evolutionary algorithms to solve network structure balance problem. First, this chapter overviews recent works on the evolutionary computations for structure balance computation and transformation in signed networks. Then, two representative memetic algorithm for the computation of structure balance in a strong definition are introduced. Next, a multilevel learning based memetic algorithm for the balance computation and the balance transformation of signed networks in a weak definition are presented. Finally, a two-step method based on evolutionary multi-objective optimization for weak structure balance are presented.

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Notes

  1. 1.

    Acknowledgement: Reprinted from Physica A: Statistical Mechanics and its Applications, 415, Sun, Y., Du, H., Gong, M., Ma, L., Wang, S., Fast computing global structural balance in signed networks based on memetic algorithm, 261–272, Copyright(2014), with permission from Elsevier.

  2. 2.

    Acknowledgement: Reprinted from Social Networks, 44, Wang, S., Gong, M., Du, H., Ma, L., Miao, Q., Du, W., Optimizing dynamical changes of structural balance in signed network based on memetic algorithm, 64–73, Copyright(2016), with permission from Elsevier.

  3. 3.

    Acknowledgement: Reprinted from Knowledge-Based Systems, 85, Ma, L., Gong, M., Du, H., Shen, B., Jiao, L., A memetic algorithm for computing and transforming structural balance in signed networks, 196–209, Copyright(2015), with permission from Elsevier.

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Authors and Affiliations

  1. Xidian University, Xi’an, Shaanxi, China

    Maoguo Gong & Shanfeng Wang

  2. Hong Kong Baptist University, Hong Kong, China

    Qing Cai & Lijia Ma

  3. Northwestern Polytechnical University, Xi’an, Shaanxi, China

    Yu Lei

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  1. Maoguo Gong
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  2. Qing Cai
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  3. Lijia Ma
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  4. Shanfeng Wang
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  5. Yu Lei
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Correspondence to Maoguo Gong .

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Gong, M., Cai, Q., Ma, L., Wang, S., Lei, Y. (2017). Network Structure Balance Analytics with Evolutionary Optimization. In: Computational Intelligence for Network Structure Analytics. Springer, Singapore. https://doi.org/10.1007/978-981-10-4558-5_4

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  • DOI: https://doi.org/10.1007/978-981-10-4558-5_4

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