Research On Maximizing The Influence Of Multi-relational Social Networks

Today,with the rapid development of social media,social networks have become the main place for people to exchange information,covering every bit of people's lives.The research on the issue of social network maximizing influence has therefore attracted everyone's attention and has become a popular research content.The issue of social network maximizing influence has very important practical significance in terms of advertising and public opinion.At present,researchers have made significant research results on the research of maximizing the influence of social networks,but these research results are concentrated in single-relationship social networks,that is,when studying this problem,users in social networks are not considered.The complex relationship between the two,and the relationship between various information that exists in the social network,but in fact this does not conform to the actual situation of the current social network.Therefore,this article starts with the actual social network,considers the multiple relationships between users and the multiple relationships between the information in the network on the influence of social network influence spread,launched a multi-relationship social network influence maximization research,and Experiments on the data set of the real social network verify the feasibility and effectiveness of the research content of this article.The main contents of this article are summarized as follows:(1)This article considers the multiple relationships that exist between users in a real social network.Based on the linear threshold model(LT),combined with the multiple relationships that exist between network users,the Multi-relationships Linear Threshold Model proposed to model the influence propagation process of the multi-user relationship social network.The MR-RRset algorithm based on the reverse reachable set is proposed to solve the problem of low computing performance of the traditional influence maximization algorithm.Finally,the experimental comparison on the real data set shows that the method proposed in this paper has a better influence propagation range and a larger calculation performance improvement.(2)This article studies the simultaneous propagation of multiple pieces of information that affect each other in the network.Based on the Independent Cascade(IC)model,the MI-IC(Multiple Information Independent Cascade)model is proposed to model the propagation of multiple types of information that simultaneously propagate in the network and interact with each other.And the mathematical characteristics of monotonicity and sub-modularity satisfied by theinfluence function under the model are studied.An influence maximization algorithm based on the MI-IC model is proposed.Experimental verification on real data sets proves that the influence maximization considering the simultaneous propagation of multiple information has certain practical significance.