Open Access

ARTICLE

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

Yirong Jiang¹, Weijin Jiang^2,3,4,*, Jiahui Chen^2,*, Yang Wang², Yuhui Xu², Lina Tan², Liang Guo⁵

1 Tonghua Normal University, Tonghua, 134002, China.
2 Key Laboratory of Hunan Province for New Retail Virtual Reality Technology, Hunan University of Commerce, Changsha, 410205, China.
3 Institute of Big Data and Internet Innovation, Hunan University of Commerce, Changsha, 410205, China.
4 School of Computer Science and Technology, Wuhan University of Technology, Wuhan, 430073, China.
5 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.

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Citations