Telecommunications Science ›› 2020, Vol. 36 ›› Issue (6): 107-118.doi: 10.11959/j.issn.1000-0801.2020162

• Research and Development • Previous Articles     Next Articles

Dynamic social network active influence maximization algorithm based on Coulomb force model

Min LU1,Guanglu CHEN1,Xiaohui YANG1,Chunlan HUANG1,Guangxue YUE1,2   

  1. 1 College of Science,Jiangxi University of Science and Technology,Ganzhou 341000,China
    2 College of Mathematical Information and Engineering,Jiaxing University,Jiaxing 314001,China
  • Revised:2020-05-18 Online:2020-06-20 Published:2020-06-18
  • Supported by:
    The National Natural Science Foundation of China(11704163);Key Research Project of Education Commission of Jiangxi Province(GJJ160594)

Abstract:

The problem of maximizing influence has become an important research content in social networks,and its influence propagation model and solving algorithm are the key core issues.In order to improve the accuracy of predicting the propagation results,the dynamic change of the number of activated nodes and the trust relationship between the nodes during the propagation process were introduced to improve the IC model.Combining the similarity between social influence and Coulomb force,a dynamic based on trust relationship was proposed,a dynamic social coulomb forces based on trust relationships (DSC-TR) model was proposed,and an optimized random greedy (RG-DPIM) algorithm was constructed to solve the problem of maximum impact.Simulation results show that the prediction accuracy of the DSC-TR model is obviously better than that of SC-B and IC models.The performance of RG-DPIM algorithm is obviously better than that of G-DPIM,IPA and TDIA algorithms.

Key words: social network, influence maximization, Coulomb force, diffusion model, trust relationship

CLC Number: 

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