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

作     者：Yirong Jiang Weijin Jiang Jiahui Chen Yang Wang Yuhui Xu Lina Tan Liang Guo 

作者机构：Tonghua Normal UniversityTonghua134002China Key Laboratory of Hunan Province for New Retail Virtual Reality TechnologyHunan University of CommerceChangsha410205China Institute of Big Data and Internet InnovationHunan University of CommerceChangsha410205China School of Computer Science and TechnologyWuhan University of TechnologyWuhan430073China School of BioinformaticsUniversity of MinnesotaTwin CitiesUSA 

出 版 物：《Journal on Internet of Things》 

年 卷 期：2019年第1卷第2期

页      面：41-53页

学科分类：0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 

基　　金：This work was supported by the National Natural Science Foundation of China(61772196 61472136) the Hunan Provincial Focus Social Science Fund(2016ZDB006) Hunan Provincial Social Science Achievement Review Committee results appraisal identification project(Xiang Social Assessment 2016JD05) Key Project of Hunan Provincial Social Science Achievement Review Committee(XSP 19ZD1005) The authors gratefully acknowledge the financial support provided by the Key Laboratory of Hunan Province for New Retail Virtual Reality Technology(2017TP1026) 

主　　题：Weak structural balance signed networks evolutionary algorithms incremental computation compressed representation 

摘      要：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 *** unbalance is an important indicator to measure the network *** paper starts from the weak structural equilibrium theorem,and integrates the work of predecessors,and proposes the weak unbalanced algorithm EAWSB based on evolutionary *** on the large symbolic networks Epinions,Slashdot and WikiElections show the effectiveness and efficiency of the proposed *** 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.