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A New Method Based on Evolutionary Algorithm for Symbolic Network Weak Unbalance

Yirong Jiang1, Weijin Jiang2,3,4,*, Jiahui Chen2,*, Yang Wang2, Yuhui Xu2, Lina Tan2, Liang Guo5
Tonghua Normal University, Tonghua, 134002, China.
Key Laboratory of Hunan Province for New Retail Virtual Reality Technology, Hunan University of Commerce, Changsha, 410205, China.
Institute of Big Data and Internet Innovation, Hunan University of Commerce, Changsha, 410205, China.
School of Computer Science and Technology, Wuhan University of Technology, Wuhan, 430073, China.
School of Bioinformatics, University of Minnesota, Twin Cities, USA.
* Corresponding Authors: Weijin Jiang. Email: .

Journal on Internet of Things 2019, 1(2), 41-53. https://doi.org/10.32604/jiot.2019.07231

Abstract

The symbolic network adds the emotional information of the relationship, that is, the “+” and “-” information of the edge, which greatly enhances the modeling ability and has wide application in many fields. Weak unbalance is an important indicator to measure the network tension. This paper starts from the weak structural equilibrium theorem, and integrates the work of predecessors, and proposes the weak unbalanced algorithm EAWSB based on evolutionary algorithm. Experiments on the large symbolic networks Epinions, Slashdot and WikiElections show the effectiveness and efficiency of the proposed method. In EAWSB, this paper proposes a compression-based indirect representation method, which effectively reduces the size of the genotype space, thus making the algorithm search more complete and easier to get better solutions.

Keywords

Weak structural balance, signed networks, evolutionary algorithms, incremental computation, compressed representation.

Cite This Article

Y. Jiang, W. Jiang, J. Chen, Y. Wang, Y. Xu et al., "A new method based on evolutionary algorithm for symbolic network weak unbalance," Journal on Internet of Things, vol. 1, no.2, pp. 41–53, 2019.

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This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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