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Social networking sites allow users to manually categorize their friends into social circles (e.g., \u201ccircles\u201d on Google+, and \u201clists\u201d on Facebook and Twitter). However, circles are laborious to construct and must be manually updated whenever a user's network grows. In this article, we study the novel task of automatically identifying users' social circles. We pose this task as a multimembership node clustering problem on a user's ego network, a network of connections between her friends. We develop a model for detecting circles that combines network structure as well as user profile information. For each circle, we learn its members and the circle-specific user profile similarity metric. Modeling node membership to multiple circles allows us to detect overlapping as well as hierarchically nested circles. Experiments show that our model accurately identifies circles on a diverse set of data from Facebook, Google+, and Twitter, for all of which we obtain hand-labeled ground truth.<\/jats:p>","DOI":"10.1145\/2556612","type":"journal-article","created":{"date-parts":[[2014,2,18]],"date-time":"2014-02-18T14:08:32Z","timestamp":1392732512000},"page":"1-28","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":208,"title":["Discovering social circles in ego networks"],"prefix":"10.1145","volume":"8","author":[{"given":"Julian","family":"Mcauley","sequence":"first","affiliation":[{"name":"Computer Science Department, Stanford University, Stanford, CA"}]},{"given":"Jure","family":"Leskovec","sequence":"additional","affiliation":[{"name":"Computer Science Department, Stanford University, Stanford, CA"}]}],"member":"320","published-online":{"date-parts":[[2014,2]]},"reference":[{"key":"e_1_2_1_1_1","volume":"200","author":"Agarwal D.","unstructured":"D. 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