基于Spark GraphX和社交网络大数据的用户影响力分析  

Analysis of user influence based on social network big data and Spark GraphX

作　　者：文馨 陈能成[1,2] 肖长江[1,2] Wen Xin;Chen Nengcheng;Xiao Changjiang(State Key Laboratory of Information Engineering in Surveying,Mapping&Remote Sensing,Wuhan University,Wuhan 430079,China;Collaborative Innovation Center of Geospatial Technology,Wuhan University,Wuhan 430079,China)

机构地区：[1]武汉大学测绘遥感信息工程国家重点实验室,武汉430079 [2]武汉大学地球空间信息技术协同创新中心,武汉430079

出　　处：《计算机应用研究》2018年第3期830-834,共5页Application Research of Computers

基　　金：湖北省自然科学基金创新群体项目(2016CFA003);国家自然科学基金资助项目(41301441);国家"863"计划资助项目(2013AA01A608)

摘　　要：利用社交网络大数据进行用户影响力分析,有助于识别网络环境中影响力强的用户实现其社会和商业价值。传统方法无法高效处理海量社交网络数据,定量准确地分析用户影响力,为解决该问题,提出一种基于PageRank算法的改进的用户影响力评价模型。综合考虑了用户连接程度和活跃程度,并以支持大规模并行图计算的Spark Graph X为工具,快速高效地实现了微博用户影响力的定量分析与评价。实验结果表明,所提方法效率更高,得到的用户影响力结果更接近真实情况。To analyze user influence based on big data from social network is helpful for recognizing users with good impact on the Internet and realizing their social and economic value.Traditional methods can not process massive social network data efficiently and analyze user influence quantitatively and precisely.To solve these problems,this paper proposed an advanced model of user influence evaluation,originating from classic PageRank algorithm,which took not only user connectivity but activity into consideration,and used Spark GraphX which supported massive parallel computing as a tool and realized analyzing influence of Weibo users quantitatively and precisely.Experiment shows that the approach proposed in this paper is a more efficient method with more precise results.

关 键 词：数据挖掘 社交网络大数据 SPARK GraphX 用户影响力分析 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]