Querying dynamic communities in online social networks

被引：5

作者：

Li Weigang ^{[1
]}

Sandes, Edans F. O. ^{[1
]}

Zheng, Jianya ^{[1
]}

de Melo, Alba C. M. A. ^{[1
]}

Uden, Lorna ^{[2
]}

机构：

[1] Univ Brasilia, Dept Comp Sci, BR-70910900 Brasilia, DF, Brazil

[2] Staffordshire Univ, Sch Comp, Stafford ST18 0AD, England

来源：

JOURNAL OF ZHEJIANG UNIVERSITY-SCIENCE C-COMPUTERS & ELECTRONICS | 2014年 / 15卷 / 02期

关键词：

Follow Model; Hadoop; MapReduce; Querying; Twitter; LINK-PREDICTION;

D O I：

10.1631/jzus.C1300281

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Online social networks (OSNs) offer people the opportunity to join communities where they share a common interest or objective. This kind of community is useful for studying the human behavior, diffusion of information, and dynamics of groups. As the members of a community are always changing, an efficient solution is needed to query information in real time. This paper introduces the Follow Model to present the basic relationship between users in OSNs, and combines it with the MapReduce solution to develop new algorithms with parallel paradigms for querying. Two models for reverse relation and high-order relation of the users were implemented in the Hadoop system. Based on 75 GB message data and 26 GB relation network data from Twitter, a case study was realized using two dynamic discussion communities: #musicmonday and #beatcancer. The querying performance demonstrates that the new solution with the implementation in Hadoop significantly improves the ability to find useful information from OSNs.

引用

页码：81 / 90

页数：10

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