%0 Journal Article
%1 jiang2013understanding
%A Jiang, Jing
%A Wilson, Christo
%A Wang, Xiao
%A Sha, Wenpeng
%A Huang, Peng
%A Dai, Yafei
%A Zhao, Ben Y.
%C New York, NY, USA
%D 2013
%I ACM
%J ACM Trans. Web
%K analyis interaction latent log mining network social user web
%N 4
%P 18:1--18:39
%T Understanding Latent Interactions in Online Social Networks
%U http://doi.acm.org/10.1145/2517040
%V 7
%X Popular online social networks (OSNs) like Facebook and Twitter are changing the way users communicate and interact with the Internet. A deep understanding of user interactions in OSNs can provide important insights into questions of human social behavior and into the design of social platforms and applications. However, recent studies have shown that a majority of user interactions on OSNs are latent interactions, that is, passive actions, such as profile browsing, that cannot be observed by traditional measurement techniques. In this article, we seek a deeper understanding of both active and latent user interactions in OSNs. For quantifiable data on latent user interactions, we perform a detailed measurement study on Renren, the largest OSN in China with more than 220 million users to date. All friendship links in Renren are public, allowing us to exhaustively crawl a connected graph component of 42 million users and 1.66 billion social links in 2009. Renren also keeps detailed, publicly viewable visitor logs for each user profile. We capture detailed histories of profile visits over a period of 90 days for users in the Peking University Renren network and use statistics of profile visits to study issues of user profile popularity, reciprocity of profile visits, and the impact of content updates on user popularity. We find that latent interactions are much more prevalent and frequent than active events, are nonreciprocal in nature, and that profile popularity is correlated with page views of content rather than with quantity of content updates. Finally, we construct latent interaction graphs as models of user browsing behavior and compare their structural properties, evolution, community structure, and mixing times against those of both active interaction graphs and social graphs.