Statistical mechanics of complex networks
Statistical mechanics of complex networks
The link prediction problem for social networks
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
ACM SIGKDD Explorations Newsletter
Group formation in large social networks: membership, growth, and evolution
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
A Handbook of Statistical Analyses Using R
A Handbook of Statistical Analyses Using R
Growth of the flickr social network
Proceedings of the first workshop on Online social networks
When social networks cross boundaries: a case study of workplace use of facebook and linkedin
Proceedings of the ACM 2009 international conference on Supporting group work
What is Twitter, a social network or a news media?
Proceedings of the 19th international conference on World wide web
Empirical comparison of algorithms for network community detection
Proceedings of the 19th international conference on World wide web
Faceted identity, faceted lives: social and technical issues with being yourself online
Proceedings of the ACM 2011 conference on Computer supported cooperative work
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
A personalized recommender system based on users' information in folksonomies
Proceedings of the 22nd international conference on World Wide Web companion
Google+ or Google-?: dissecting the evolution of the new OSN in its first year
Proceedings of the 22nd international conference on World Wide Web
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Online social networks like Google+, Twitter, and Facebook allow users to build, organize, and manage their social connections for the purposes of information sharing and consumption. Nonetheless, most social network users still report that building and curating contact groups is a time consuming burden. To help users overcome the burdens of contact discovery and grouping, Google+ recently launched a new feature known as "circle sharing." The feature makes it easy for users to share the benefits of their own contact curation by sharing entire "circles" (contact groups) with others. Recipients of a shared circle can adopt the circle as a whole, merge the circle into one of their own circles, or select specific members of the circle to add. In this paper, we investigate the impact that circle-sharing has had on the growth and structure of the Google+ social network. Using a cluster analysis, we identify two natural categories of shared circles, which represent two qualitatively different use cases: circles comprised primarily of celebrities (celebrity circles), and circles comprised of members of a community (community circles). We observe that exposure to circle-sharing accelerates the rate at which a user adds others to his or her circles. More specifically, we notice that circle-sharing has accelerated the "densification" rate of community circles, and also that it has disproportionately affected users with few connections, allowing them to find new contacts at a faster rate than would be expected based on accepted models of network growth. Finally, we identify features that can be used to predict which of a user's circles (s)he is most likely to share, thus demonstrating that it is feasible to suggest to a user which circles to share with friends.