Performance Analysis of User Influence Algorithm under Big Data Processing Framework in Social Networks

被引：0
|
作者：
Quan, Yong [1 ]
Zhang, Liang [1 ]
Jia, Yan [1 ]
Zhou, Bin [1 ]
机构：
[1] Natl Univ Def Technol, Coll Comp, Changsha, Hunan, Peoples R China
来源：
2018 IEEE 3RD INTERNATIONAL CONFERENCE ON CLOUD COMPUTING AND BIG DATA ANALYSIS (ICCCBDA) | 2018年
基金：
中国国家自然科学基金;
关键词：
influence measurement; performance analysis; big data technique; distributed computing; social networks;
D O I：
暂无
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
Social influence plays an essential role in spreading information within online social networks, and can be modeled or measured by analyzing various social networking data, such as published content, users' attributes or interactions among them. Because of the massive social data, researchers often fail to quantify user influence in an accurate and high efficient way. Big data technique can be adopted to alleviate this problem. In this paper, we introduce a kind of classical individual influence algorithm, and implement two parallel versions of this algorithm based on different big data processing framework Experiment results on a large-scale real dataset demonstrate that the computational efficiency of influence algorithm can be improved significantly in massive data sets by virtue of big data processing framework
引用
收藏
页码：180 / 185
页数：6
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