微博社交网络的对称程度实证分析

计算机应用 ›› 2014, Vol. 34 ›› Issue (12): 3405-3408.

微博社交网络的对称程度实证分析

康泽东¹,余旌胡¹,丁义明²

1. 武汉理工大学理学院,武汉 430070
2. 中国科学院武汉物理与数学研究所,武汉 430071

收稿日期:2014-05-27 修回日期:2014-07-22 出版日期:2014-12-01 发布日期:2014-12-31
通讯作者: 康泽东
作者简介:康泽东(1989-）,男,河南信阳人,硕士研究生,主要研究方向:统计学习理论与方法；余旌胡(1967-）,女,湖南岳阳人,教授,博士,主要研究方向:马氏过程及其应用、统计学习理论与方法；丁义明(1972-）,男,江西丰城人,研究员,博士,主要研究方向:系统科学理论与方法、数据分析、离散动力系统。

Empirical analysis of symmetry degree for micro-blog social network

KANG Zedong¹,YU Jinghu¹,DING Yiming²

1. School of Science, Wuhan University of Technology, Wuhan Hubei 430070, China;
2. Wuhan Institute of Physics and Mathematics, Chinese Academy of Sciences, Wuhan Hubei 430071,China

Received:2014-05-27 Revised:2014-07-22 Online:2014-12-01 Published:2014-12-31
Contact: KANG Zedong

摘要/Abstract

摘要：

Twitter和Sina微博注册用户构成关注关系社交网络,运用一种对称程度来研究其对称性随社交圈子规模变化的规律。首先根据收集的100万条新浪用户之间的关注关系和236个Twitter用户及其之间的关注关系来构建初始社交网络,选取其中具有明显对称性的连通子网络作为研究的主要对象,通过去除法得到:影响社交网络最大连通子网络对称性的主要因素是大V用户和可忽略用户。其次,采用比较分析法得出Twitter的大V用户构成的社交子网络对称性较强。最后,从功能定位方面分析了两种微博的不同；通过对初始网络的所有连通子网络的对称程度的研究,得出社交圈规模越小、相应的对称性越强的结论。

Abstract:

While Twitter and Sina micro-blogs abundant registered users formed a social network of focusing relationship, by using the degree of symmetry its change regulation with the scale of the social circle was studied. Firstly, based on the collection of 1000000 focusing relationships among the Sina micro-blog users and 236 Twitter users as well as their focusing relationships, the initial social network was established. Here focus lied on the connected sub-networks which had obvious symmetrical connects, then the elimination method was applied to obtain these conclusions: The major factors that affect the symmetry of the maximum connected sub-networks are those who are called big V users and negligible users. After that, comparative analysis method was used to find out that the sub-network consisted of the big V users in Twitter has a stronger symmetry. Finally, the difference between these two kinds of micro-blogs was figured out in terms of functional localization. Through the researches on the symmetry of all connected sub-networks within the initial network, the result shows that when the scale of a public social circle decreases, the corresponding symmetry becomes stronger.

中图分类号:

TP399

康泽东余旌胡丁义明. 微博社交网络的对称程度实证分析[J]. 计算机应用, 2014, 34(12): 3405-3408.

KANG Zedong YU Jinghu DING Yiming. Empirical analysis of symmetry degree for micro-blog social network[J]. Journal of Computer Applications, 2014, 34(12): 3405-3408.

参考文献

［1］FAN C, HUANG S, LI Y. Study on microblog social network community detection［J］. Microcomputer and Its Applications: Method and Technique of Version, 2012,31(23):67-70. (范超然, 黄曙光, 李永成. 微博社交网络社区发现方法研究［J］. 微型机与应用: 技术与方法版, 2012,31(23):67-70.)
［2］YIN S. A research of user relations properties based on a complex network of microblog［J］.Journal of Southwest China Normal University: Natural Science Edition, 2011,33(6):57-61. (尹书华. 基于复杂网络的微博用户关系网络特性研究［J］. 西南师范大学学报:自然科学版,2011,36(6):57-61.)
［3］YIN Z, LIU Z. The study of asymmetric friendship’s influence on information diffusion in social network［EB/OL］. ［2011-12-27］.http://www.paper.edu.cn/releasepaper/content/201112-758. (尹志军, 刘志晗. 社交网络中好友关系的非对称性对信息传播影响的研究［EB/OL］.［2011-12-27］. http://www.paper.edu.cn/releasepaper/content/201112-758.)
［4］XIAO Y. Structural symmetry of the figure and its application in data management issues［D］. Shanghai: Fudan University,2009.(肖仰华. 图结构对称性及其在数据管理问题中的应用［D］. 上海: 复旦大学, 2009.)
［5］WU W. Graph symmetry theory in application of social network analysis of a number of important issues［D］. Shanghai: Fudan University,2010.(吴文涛. 图对称理论在社会网络分析若干重要问题中的应用［D］. 上海: 复旦大学, 2010.)
［6］SIEGLER M G.Twitters golden ratio (that no one likes to talk about)［EB/OL］.(2009-08-26).http://techcrunch.com/2009/08/26/twitters-golden-ratio-that-no-one-likes-to-talk-about/
［7］GARRIDO A. Symmetry in complex networks[J]. Symmetry Measures on Complex Networks, 2011,3(1):1-15.
［8］ZAFARANI R, ABBASI M A, LIU H. Social media mining: an introduction［M］. Cambridge: Cambridge University Press, 2014.
［9］JIN X, XIE B, ZHU J. Microblog network public opinion diffusion based on complex network analysis［J］. Journal of Jilin University: Engineering and Technology Edition, 2012,42(S1):271-275.(金鑫, 谢斌, 朱建明. 基于复杂网络分析的微博网络舆情传播［J］. 吉林大学学报: 工学版,2012,42(S1):271-275.)
［10］ZHANG H. Natural language processing & information retrieval sharing platform[DB/OL].[2014-06-14].http://www.nlpir.org/?action-viewnews-itemid-230.(张华平. 自然语言处理与信息检索共享平台［DB/OL］.[2014-06-14].http://www.nlpir.org/?action-viewnews-itemid-230.)
［11］Lilimu123. The date of twitter (973networks)[DB/OL].[2014-06-14].http://www.datatang.com/data/43971#.(Lilimu123. Twitter数据(973networks)［DB/OL］.[2014-06-14].http://www.datatang.com/data/43971#.)
［12］WANG H,HUANG Q,LI C,et al. Graph theory algorithm and its Matlab implementation［M］. Beijing: Beihang University Press,2010:4-6.(王海英, 黄强, 李传涛,等. 图论算法及其Matlab实现［M］. 北京: 北京航空航天大学出版社,2010:4-6.)
［13］Sohu IT. The Sina Weibo in Twitter engineers’ eyes: Differences mainly reflected in the four aspects ［G/OL］.[2014-06-14].(搜狐IT. Twitter工程师眼中的新浪微博: 主要体现四点不同［G/OL］.[2014-06-14].http://labs.chinamobile.com/news/94054_p2.)

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