A Users’ Real-time Influence Algorithm Of Social Network Based On PageRank

Social networks (Social Network Service, SNS) is an important field in theInternet today. SNS is a platform based on friend’s relationship. In this platform,people can share, get and broadcast information. With the development of themobile Internet, more and more people can share messages in this platformanywhere. SNS is playing a very important role in the message communicationplatform. There are many famous SNS, such as Facebook, Twitter, YouTube andso on. Since the social network is based on real friends’ relationship, it has bothcharacters of media communication and social network.AS a famous social network in China, Sina micro-blogging has over threemillion users, mass information are released on this platform every day. Sinamicro-blogging network plays a very important role in the dissemination ofinformation, even beyond traditional communication media. For example, thenews of "Jeremy Lin" is spread in the Sina micro-blogging at first. Therefore theresearches of Sina micro-blogging network have significant commercial value and the value of applications, it will help us to understand the model of messagepropagation.Human behavior in the internet is also subjective. Based on the humandynamics theories, we advanced a new algorithm to evaluate Micro-bloggingusers’ real-time influence. The main work of this paper is presented asfollowing：First, we described complex networks’ originate, the basic concept and twobasic characteristics. The basic characteristics are small-world effect andscale-free characteristics, which are different from random network. Somestudies have shown that the online social networks also have the characteristicsof small-world effect and scale-free, which means online social network is akind of complex network. Then discussed the development of the social networkanalysis, and focused on the "six degrees of separation theory" and the "150rule". The paper also made a brief introduction of the development and researchstatus of online social networks.Second, through Sina micro-blogging API, we got a lot of user information.In this data set, we statistical analyzed the behavior of retweet, the experimentalresults show that the distribution of retweet behaviors follows power-lawdistribution, which is consistent with research in the field of human dynamics,that the distribution of human behavior follows power-law distribution, and has the characteristics of fat tails.Third, based on the basic idea of PageRank algorithm, we advanced analgorithm of user real-time influence. This algorithm called Micro-bloggingUser Rank, namely MURank. MURank based on the distribution of users’retweet and the network structure to calculate the real-time influence of a user.Through evaluating the influence of node, we can quickly find the key nodes inthe dissemination of information to understand the micro-blogging networknews dissemination and contribute to a micro-blogging influence.Finally, we used the traditional PageRank algorithm and the number of fansto evaluate the influence of user respectively, the experiments show thatMURank has a better character of real-time than the others.