Analysis of user influence in social network based on behavior and relationship

Online social network, has become an important platform for people to communicate and get information. Micro-blog, the convenient and quick platform showed strong development momentum. The paper provided an algorithm to analyze the micro-blog user's influence. Through sina weibo open platform to obtain the information of user relation data and the user data of the time published micro-blog. According to PageRank algorithm we can calculate user's influence. But all micro-blogging users are seen as the same and shared influence in the PageRank algorithm, not take into account the impact of user activity, which does not meet the actual situation of micro-blogging. Therefore we add the user activity to PageRank algorithm to obtain an improved PageRank algorithm. By comparing the experimental results of these two algorithms, we can see that when a user activity is high, even though he has little fans , it can also obtain a higher user influence value; on the contrary, when a user activity is low ,even though he has many fans, its user influence value is lower. It is proved that in the calculation of the influence of micro-blogging users, the improved algorithm is more realistic than the original algorithm.