Influence maximization algorithm based on group trust and local topology structure

Influence maximization is one of the important contents of social network analysis. Many classical influence propagation models assume that there is a stable information propagation phenomenon between adjacent users, and do not consider the influence of internal structure information of the network on the actual information propagation. Therefore, an influence maximization algorithm based on group trust and local topology structure is proposed. In order to make full use of the important role of group in information propagation, the concepts of intra-group connectivity, inter-group diffusion and group trust are defined based on the characteristics such as group tightness. Then, an influence propagation algorithm based on the local topological structure of the group is proposed to extract the local structure information of different topological positions in the group, and calculate the propagation probability between users. Finally, the seed nodes were selected according to the credibility ranking of the group for influence propagation. Experiments on multiple data sets show that compared with other algorithms, the algorithm can achieve higher propagation efficiency and wider influence effect, which verifies the rationality and effectiveness of the method.

Automated summary

This study proposes an influence maximization algorithm based on group trust and local topology structure. By defining characteristics such as group tightness, it extracts local structure information from different topological positions within the group and calculates the propagation probability between users. Experiments show that the algorithm can achieve higher propagation efficiency and wider influence effects across multiple datasets.