摘要： 在已有PageRank算法构建的微博用户影响力评估模型中，存在用户自身属性信息欠缺以及在用户不活跃期间其影响力被误判下降的问题。为此，综合考虑用户自身的属性，基于用户的活跃度、认证信息及博文质量来确定其自身的基本影响力，通过引入用户博文的传播率挖掘用户的潜在影响力，结合用户不同好友的质量，基于改进的PageRank算法构建微博用户影响力评估算法。实验结果表明，与改进BWPR算法相比，该算法准确率、召回率和F值分别提高13.5%、10.1%和12.3%，能准确、客观地反映微博用户的实际影响力，可为社交网络中的意见领袖挖掘、信息传播和舆论引导等研究提供参考。

关键词: 微博, 社交网络, 影响力, PageRank算法, 传播率

Abstract: The current PageRank algorithm based microblog user influence evaluation model has many problems,such as the lack of users' attributes and the incorrect evaluation of users' influence during their inactive period.In this paper,the attributes of users are comprehensively considered,and their basic influence is confirmed based on users' activeness,authentication information and blog quality.Then,according to the blog dissemination rate,this paper further excavates the potential influence of users.Ultimately,with the evaluation of the quality of different microblog friends,a microblog user influence evaluation algorithm is established on the basis of improved PageRank algorithm.Experimental results show that compared with BWPR improved algorithm,the accuracy,recall rate and F value of the proposed method are increased by 13.5%,10.1% and 12.3% respectively.It can reflect the actual influence of users in a more accurate and objective way,which can provide references to the researches on opinion leader identification,information dissemination and the guidance of public opinions in social networks.

Key words: microblog, social network, influence, PageRank algorithm, dissemination rate

中图分类号: 

• TP393