摘要
PageRank算法在计算用户影响力方面只考虑用户间的跟随关系,导致计算结果准确性低下。为此,提出一种将用户行为因素与PageRank算法相结合的URank算法。利用网络中用户发布信息的转发率、评论率以及是否认证等行为因素,综合用户自身质量与追随者质量,得到用户影响力。基于SIR传播模型的实验结果表明,URank算法在计算准确性方面优于PageRank算法。
In the calculation of user influence, the PageRank algorithm considers only the following relation among users,which leads to the low accuracy of the calculation results. Therefore, a URank algorithm combining user behavior factors with PageRank algorithm is proposed. By using the factors such as forwarding rate, comment rate and authentication,the user＇ s quality can be obtained by combining the quality of users and the quality of followers. Experimental results show that based on the SIR propagation model,URank algorithm is superior to PageRank algorithm in computational accuracy.
出处
《计算机工程》
CAS
CSCD
北大核心
2017年第12期155-159,共5页
Computer Engineering
基金
吉林省科技发展计划重点科技攻关项目(20150204036GX)
