[1]李 勇.一种改进的微博用户影响力分析算法[J].计算机技术与发展,2020,30(08):27-33.[doi:10. 3969 / j. issn. 1673-629X. 2020. 08. 005]
　LI Yong.An Improved Algorithm of Microblog User Influence Analysis[J].,2020,30(08):27-33.[doi:10. 3969 / j. issn. 1673-629X. 2020. 08. 005]
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一种改进的微博用户影响力分析算法()

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
30
期数:
2020年08期
页码:
27-33
栏目:
智能、算法、系统工程
出版日期:
2020-08-10

文章信息/Info

Title:
An Improved Algorithm of Microblog User Influence Analysis
文章编号:
1673-629X(2020)08-0027-07
作者:
李 勇
解放军信息工程大学,河南 郑州 450000
Author(s):
LI Yong
PLA Information Engineering University,Zhengzhou 450000,China
关键词:
微博; 用户影响力; 粉丝影响力; PageRank; MBUInfluence
Keywords:
microblog; user influence; fans爷 influence; PageRank; MBUInfluence
分类号:
TP391
DOI:
10. 3969 / j. issn. 1673-629X. 2020. 08. 005
摘要:
针对现有用户影响力分析算法的不足,基于网页结构与微博用户网络的相似性,通过分析微博用户之间的行为交互以及传统的 PageRank 算法,提出一种新的 MBUInfluence 算法来对用户影响力进行分析。 该算法结合微博用户的活跃度、积极度和传播度等特点,将微博用户影响力定义为由用户自身行为权重和粉丝的影响力构成。 通过分析 PageRank 算法,结合微博转发率、评论率、点赞率等特征,制定了微博用户的影响比例函数,形式化定义了微博用户行为权重和粉丝影响力。 通过采集新浪微博实验数据,从转发数、粉丝数、新增粉丝数等方面与传统的 FansRank、ForwardRank、PageRank 等影响力排名算法进行对比,从不同角度分析该算法的实际应用效果,并得出微博的用户影响力与单位时间内新增粉丝的数量和质量相关的结论。
Abstract:
Aimed at the deficiencies of conventional user influence analysis algorithms,and based on the similarity between web structure and microblog? ? user network, we propose a novel MBUInfluence algorithm to analyze user influence by analyzing microblog user interactions and the conven-? tional PageRank algorithm. In this algorithm,the influence of microblog user is defined as the combination of the weight of their own behaviors? ? ?and the influence of their fans in combination with such key microblog user features as activeness, vibrancy and popularity. Combining with the charact-eristics of microblog forwarding rate, comment rate and thumbing up rate, the influence proportion function of microblog user is formula-? ?ted by analyzing PageRank algorithm,and the behavior weight of microblog users and the influence of fans are formally defined. Comparing with the traditional ranking algorithms such as FansRank,Forward Rank and PageRank in terms of number of forwards,number of fans,number of new fans and so on,the practical application effect of the proposed algorithm is analyzed from different angles,and the conclusion that microblog user influence is related to the quantity and quality of new fans in a unit time is drawn.

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[1]舒琰,向阳,张骐,等.基于PageRank的微博排名MapReduce算法研究[J].计算机技术与发展,2013,(02):73.
　SHU Yan,XIANG Yang,ZHANG Qi,et al.Research on MapReduce Algorithm of Micro Blog Ranking Based on PageRank[J].,2013,(08):73.
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[3]苏小英[][],孟环建[]. 基于神经网络的微博情感分析[J].计算机技术与发展,2015,25(12):161.
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[5]房旋[],陈升波[],宫婧[][],等. 基于社交影响力的推荐算法[J].计算机技术与发展,2016,26(06):31.
　FANG Xuan[],CHEN Sheng-bo[],GONG Jing[][],et al. A Recommendation Algorithm Based on Social Influence[J].,2016,26(08):31.
[6]陈阳[],邵曦[],赵海博[]. 基于UR-LDA的微博主题挖掘[J].计算机技术与发展,2017,27(06):173.
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更新日期/Last Update: 2020-08-10