摘要
Facebook、Twitter、人人网和新浪微博等社交网站逐渐成为互联网上用户数量最多、最受欢迎的网站.近年来,国内外已有大量研究工作深入考察在线社会网络的拓扑结构和用户行为,这对理解人类的社会行为、改进现有的网站系统和设计新的在线社会网络应用具有重要意义.文中从测量角度对在线社会网络的拓扑结构、用户行为和网络演化等方面进行了综述,总结了常见的测量方法和典型的网络拓扑参数,着重介绍了用户行为特征、用户行为对网络拓扑的影响以及网络的演化.可以看出,随着研究的深入,在线社会网络的新特征逐渐被大家认识和理解,包括好友少的用户的交流范围集中在小部分好友,而好友多的用户联系的好友更均匀;用户之间的交互减小了在线社会网络的聚类系数,使网络结构更松散;边的生成受优先连接和临近偏倚的共同影响;小社团倾向于和大社团合并,大社团倾向于分裂为两个规模相当的小社团等.
Social network sites,like Facebook,Twitter,Renren and Sina Weibo,are now becoming increasingly popular on the Internet.For the past few years,numerous research have been made to investigate the topological structure and user behaviors of online social networks,which is quite important for the understanding of human social behaviors,the improvement of current Website systems and the design of online social networks＇ new applications.This paper provides an overview of online social networks＇ topology,user behaviors and network evolution.It also summarizes several common measuring methods and typical topological features;highlights user behavior characteristics and their impacts on network topology,and the network evolution.The conclusion can been drawn that as research progresses,the new characteristics of online social networks are gradually recognized and understood：users with a smaller number of correspondents tend to interact more with a subset of correspondents,while users with a very large number of correspondents actually spread their activity evenly across all of the correspondents;users＇ interactions decrease the clustering coefficient and loose the connections between neighbors; edge creation is influenced by both preferential attachment and proximity bias; small communities tend to merge with large ones which tend to split into two comparable size communities.
出处
《计算机学报》
EI
CSCD
北大核心
2014年第1期165-188,共24页
Chinese Journal of Computers
基金
国家支撑计划(2011BAK08B05-02)
国家科技重大专项基金(2012ZX03005001)
国家自然科学基金(61170292
60970104)
国家"八六三"高技术研究发展计划项目基金(2013AA013302)
国家"九七三"重点基础研究发展规划项目基金(2009CB320501
2012CB315803)资助~~
关键词
在线社会网络
测量
网络拓扑
用户行为
演化
社交网络
online social networks
measurement
network structure
user behavior
evolution
social networks
