Research on social network influence maximization algorithm based on time sequential relationship

• Published in: Tongxin Xuebao Vol. 41; pp. 211 - 221
• Main Authors: Jing CHEN, Ziyi QI
• Format: Journal Article
• Language: Chinese
• Published: Editorial Department of Journal on Communications 01.10.2020
• Subjects: time sequential social network;influence maximization;information propagation model;greedy algorithm;heuristic algorithm
• ISSN: 1000-436X

For the time sequential relationship between nodes in a dynamic social network,social network influence maximization based on time sequential relationship was proved.The problem was to find k nodes on a time sequential social network to maximize the spread of information.Firstly,the propagation probability between nodes was calculated by the improved degree estimation algorithm.Secondly,in order to solve the problem that WCM models based on static social networks could not be applied to time sequential social networks,an IWCM propagation model was proposed and based on this,a two-stage time sequential social network influence maximization algorithm was proposed.The algorithm used the time sequential heuristic phase and the time sequential greedy phase to select the candidate node with the largest influence estimated value inf (u) and the most influential seeds.At last,the efficiency and accuracy of the TIM algorithm were proved by experiments.In addition,the algorithm combines the advantages of the heuristic