Analysis Of Social Influence Based On Percolation Theory

The issue of maximizing influence is one of the hot issues in the study of complex networks.It's of great significance for understanding the dissemination mechanism of network information and controlling rumor.In recent years,based on the percolation theory,the problem of maximizing the node identification has attracted a lot of attention.However,this method does not consider the influence of the propagation of trust on the maximization of influence.This paper introduces the trust transfer function to depict the phenomenon that trust value and distrust value decreases and increase,respectively,and uses the percolation theory to solve the node joint propagation strength index to excavate the influencer.Experiments show that the proposed algorithm outperforms other heuristic benchmark algorithms.The other problem proposes a new group consensus view search model for online social networking question answering system,which comprehensively considers the impact strength and correctness of the respondents in social networks.Based on the traditional WEBER model,this paper proposes the concepts of the viewpoint negotiation distance and the user viewpoint support domain.In the process of selecting group consensus views,a voting mechanism is introduced to ensure the correctness of group users while minimizing the user group negotiation distance.The results show that the group consensus view is used as the recommended benchmark for the question answering system,and the user's perspective of the negotiation distance and the average of the correctness are used as evaluation indicators.The best answer can be selected to deviate from the group consensus view,and the answer is recommended.