Face Social Networking Sites’s Data Mining Application And Research-user Relationship Analysis

Social networking sites (Social Website, referred to as SW) are an important area of today’sInternet site. Social networking sites provide a platform for people on social networking sites tomake friends and build relationships with friends, this action are formed the user’s social network(Social Network Service). in this platform，the user can communication with friends and enhancethe relationship with other friends. With the development of mobile Internet, more and more peopleon social networking sites can be sent and share information anytime. Social networking sites are avery important platform for the dissemination of news. The famous social networking websiteFaceBook, Twitter, YouTube, RenRen, Sina Weibo. As the social networking site among users is tobuild up the network structure based on real friends, so it also has properties of social networkingand net.Research focus of this paper is divided into three parts:The first part analyzes the relationship between the user’s social networking site users of socialnetworks. Based on the general attributes of online social networking sites and relationship research,analyze social network user attributes and behaviors. Particularly by the number of fans on themicroblogging network users, concerned number, Comment number, forwarding informationnumber studies found that social networking site users sparse network of relationships, therandomness, small world and so on.The second part study users influence algorithm in social network. According to previouspeople on social networks influence algorithm, study the PageRank algorithm ideas. And from thisalgorithm to analyze the previous people algorithm is obtained user influence only based on theuser’s out-degree and in-degree. Thus, this paper presents a user influence algorithm base onentity-relationship, construct the entity-relationship influence algorithm model.Proposed influentialfactors affecting users and calculate user influence by the PageRank algorithm.and study the socialnetworking site user’s social network, small factions detection. By analysis of existing communitiesalgorithm, according the user’s social network factional characteristics, obtained social networkusers factions by using the aggregation algorithm, and the central figure of factions.Based on the user influence algorithms and factional detection algorithms, the third partdescribes social networking sites user relationship analysis system architecture, database design andsystem interface design. Get social network users data from the social networking site,get usersinfluence top ten users in the social network and users of the various factions.