Dynamic social network active influence maximization algorithm based on Coulomb force model - Min LU; Guanglu CHEN; Xiaohui YANG; Chunlan HUANG; Guangxue YUE (Dianxin kexue)
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.