DocumentCode
531481
Title
Empirical Analysis and Multiple Level Views in Massive Social Networks
Author
Ye, Qi ; Wu, Bin ; Gao, Yuan ; Wang, Bai
Author_Institution
Sch. of Comput. Sci., Beijing Univ. of Posts & Telecommun., Beijing, China
Volume
1
fYear
2010
fDate
Aug. 31 2010-Sept. 3 2010
Firstpage
541
Lastpage
544
Abstract
With the emergence of massive social media, massive social networks have led to a huge interest in data analysis. In this paper, we propose an empirical study on several massive social networks including 4 mobile call graphs, a fixed-line call graph, two co-authorship networks and two Email networks. We find that call graphs tend to be more locality than the co-authorship networks and Email networks. To our surprise, we even find that there is no significant relations between community sizes and their quality scores for most extracted communities. We also find that some very huge community with high mean quality values, and we can not find the universal "V" shape in their mean quality values.
Keywords
data analysis; graph theory; information networks; social networking (online); coauthorship network; data analysis; email network; empirical analysis; fixed line call graph; massive social media; massive social network; mean quality value; mobile call graph; multiple level view; universal V shape; Call graphs; Community; Graph Mining; Social Network Analysis;
fLanguage
English
Publisher
ieee
Conference_Titel
Web Intelligence and Intelligent Agent Technology (WI-IAT), 2010 IEEE/WIC/ACM International Conference on
Conference_Location
Toronto, ON
Print_ISBN
978-1-4244-8482-9
Electronic_ISBN
978-0-7695-4191-4
Type
conf
DOI
10.1109/WI-IAT.2010.48
Filename
5616338
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