Research On Influence Maximization Of Social Network Based On Linear Threshold Model

In recent years,the social network is becoming more and more important in the Internet,and it has been widely studied.Because people are more likely to share their thoughts and ideas in social networks.Social network contains a lot of valuable information,which can make many business activities by using social networks,such as advertising,public opinion analysis,information propagation,and so on.The Mining some users from social networks,using these users for product promotion and information propagation is becoming more and more popular,it has formed a class of problem research,social network influence maximization.The influence maximization of social network is such a problem,identifying some of the most influential people in the social network,which as the source of the initial propagation,who can maximize the spread of information.However,the existing methods ignore human interests in social networks,so these methods and models are unreasonable.Because men may have multiple interests,and the sensitivity of each interest is not the same.In addition,these methods also ignore the content of the information to be disseminated,because people of different backgrounds do not have the same information for different information,so the same crowd has different influence on different information.In view of the existing research work,this paper points out the shortcomings in these work.This paper mainly focused on the existing work did not take into account the user's interest,and did not take into account the content of the information to be disseminated,so mining out users are not able to maximize the impact of disseminated information.This paper solves the above two main problems,combined with previous research work,redefines the influence of social network maximization,puts forward the idea of maximizing the influence of social network with interest group,and designs a method to integrate social network in the interest group.This paper propose a new method to measure the influence of social network of multi-interest group.Then,a novel IING(Identifying Influential Nodes Greedy Algorithm)algorithm is proposed to compute the most influential users,IING algorithm can make some of the users to mining as the initial information dissemination source,the information can be more people to accept.Finally,the method of identifying the interest group in the social network is experimented in the real data set,which proves the validity of the method.And the experimental results show that the IING algorithm proposed in this paper is superior to the existing method in time and effect.