基于静电场理论和PageRank算法的微博用户相关性分析

微博作为最流行的网络社交媒体之一,其用户之间的相关性分析,是社交网络应用中社区发现、影响传播和行为建模等问题的重要基础.以静电场理论和PageRank算法为基础,利用其简单可靠、基础坚实的良好性质,提出了微博用户重要程度、用户行为距离的概念,并结合微博文本词汇相似度,给出了微博用户相关性的度量方法.实验结果表明,提出的微博用户相关性分析方法具有高效性和准确性.

Association analysis of microblog users based on the electrostatic theory and PageRank algorithm

Microblogging is one of the most popular web social media.Analyzing the associations between microblog users is the basis for community discovery, influence propagation and behavior modeling in social network applications.In this paper, we propose the concept of importance degree of microblog users and distance based on the electrostatic theory and PageRank algorithm while making use of their reliability with solid foundation.By further incorporating the similarity of microblog messages, we give the ultimate measuring method for the associations between microblog users.Experimental results show the efficiency and precision of our method proposed in this paper.