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Web"],"published-print":{"date-parts":[[2013,10]]},"abstract":"<jats:p>\n            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\n            <jats:italic>latent interactions<\/jats:italic>\n            , that is, passive actions, such as profile browsing, that cannot be observed by traditional measurement techniques.\n          <\/jats:p>\n          <jats:p>\n            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\n            <jats:italic>latent interaction graphs<\/jats:italic>\n            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.\n          <\/jats:p>","DOI":"10.1145\/2517040","type":"journal-article","created":{"date-parts":[[2013,11,6]],"date-time":"2013-11-06T14:09:19Z","timestamp":1383746959000},"page":"1-39","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":74,"title":["Understanding latent interactions in online social networks"],"prefix":"10.1145","volume":"7","author":[{"given":"Jing","family":"Jiang","sequence":"first","affiliation":[{"name":"Peking University and State Key Laboratory of Software Development Environment Beihang University, China"}]},{"given":"Christo","family":"Wilson","sequence":"additional","affiliation":[{"name":"University of California, Santa Barbara, CA"}]},{"given":"Xiao","family":"Wang","sequence":"additional","affiliation":[{"name":"Peking University, China"}]},{"given":"Wenpeng","family":"Sha","sequence":"additional","affiliation":[{"name":"Peking University, China"}]},{"given":"Peng","family":"Huang","sequence":"additional","affiliation":[{"name":"Peking University, China"}]},{"given":"Yafei","family":"Dai","sequence":"additional","affiliation":[{"name":"Peking University, China"}]},{"given":"Ben Y.","family":"Zhao","sequence":"additional","affiliation":[{"name":"University of California, Santa Barbara, CA"}]}],"member":"320","published-online":{"date-parts":[[2013,11]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/1242572.1242685"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/1644893.1644900"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/1753326.1753613"},{"key":"e_1_2_1_4_1","volume-title":"Proceedings of the 9th USENIX Conference on Networked Systems Design and Implementation.","author":"Cao Q.","unstructured":"Cao , Q. , Sirivianos , M. , Yang , X. , and Pregueiro , T . 2012. Aiding the detection of fake accounts in large scale social online services . In Proceedings of the 9th USENIX Conference on Networked Systems Design and Implementation. Cao, Q., Sirivianos, M., Yang, X., and Pregueiro, T. 2012. Aiding the detection of fake accounts in large scale social online services. In Proceedings of the 9th USENIX Conference on Networked Systems Design and Implementation."},{"key":"e_1_2_1_5_1","volume-title":"Proceedings of the International AAAI Conference on Weblogs and Social Media (ICWSM).","author":"Cha M.","unstructured":"Cha , M. , Haddadi , H. , Benevenuto , F. , and Gummadi , K . 2010. Measuring user influence in twitter: The million follower fallacy . In Proceedings of the International AAAI Conference on Weblogs and Social Media (ICWSM). Cha, M., Haddadi, H., Benevenuto, F., and Gummadi, K. 2010. Measuring user influence in twitter: The million follower fallacy. 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