计算机应用 ›› 2015, Vol. 35 ›› Issue (4): 1017-1020.DOI: 10.11772/j.issn.1001-9081.2015.04.1017

• 人工智能 • 上一篇    下一篇

基于HRank的微博用户影响力评价

贾冲冲, 王名扬, 车鑫   

  1. 东北林业大学 信息与计算机工程学院, 哈尔滨 150040
  • 收稿日期:2014-10-20 修回日期:2014-12-04 出版日期:2015-04-10 发布日期:2015-04-08
  • 通讯作者: 王名扬
  • 作者简介:贾冲冲(1990-),男,山东菏泽人,硕士研究生,主要研究方向:并行分布式计算、高性能计算; 王名扬(1980-),女,山东泰安人,副教授,博士,主要研究方向:数据挖掘、社交网络挖掘; 车鑫(1988-),男,黑龙江哈尔滨人,硕士研究生,主要研究方向:数据挖掘。
  • 基金资助:

    国家自然科学基金资助项目(71473034);中央高校基本科研业务费专项资金资助项目(2572014DB05);中国博士后科学基金资助项目(2012M520711)。

Evaluation of microblog users' influence based on Hrank

JIA Chongchong, WANG Mingyang, CHE Xin   

  1. College of Information and Computer Engineering, Northeast Forestry University, Harbin Heilongjiang 150040, China
  • Received:2014-10-20 Revised:2014-12-04 Online:2015-04-10 Published:2015-04-08

摘要:

针对微博社交网络平台中的用户影响力评价问题,提出了一种基于HRank的评价算法。该算法将评价科学家科研绩效影响力的判定参数H指数引入进来,构造出能反映用户影响覆盖度的粉丝H指数和用户微博受追捧程度的微博被转发H指数,以分别表征用户的静态特征和在微博平台上的动态行为特征。在此基础上,结合粉丝H指数和微博被转发H指数构建出对用户影响力进行综合评价的HRank模型。粉丝数与用户影响力的相关性不是很强,同样数据集下相对PageRank,HRank用户影响力模型与新浪用户影响力官方排名更为接近,可有效实现对微博用户影响力的客观评判。

关键词: 用户影响力, 微博, PageRank, H指数

Abstract:

An evaluation algorithm based on HRank was proposed to evaluate the users' influence in microblog social networking platform. By introducing H parameter which used for judging the scientific research achievements of scientists and considering the user's followers and their microblog forwarding numbers, two new H-index models of followers H-index and microblog-forwarded H-index were given. Both of them could represent the users' static characters and their dynamic activities in microblog, respectively. And then the HRank model was established to make comprehensive assessment on users' influence. Finally, the experiments were conducted on Sina microblog data using the HRank model and the PageRank model, and the results were analyzed by correlation on users' influence rank and compared to the results given by Sina microblog. The results show that user influence does not have strong correlation with the number of fans, and the HRank model outperforms the PageRank model. It indicates that the HRank model can be used to identify users influence effectively.

Key words: user influence, microblog, PageRank, H-index

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