Discovering social circles in ego networks
By Julian McAuley, Jure Leskovec. ACM Transactions on Knowledge Discovery from Data, 2012
396 citations recorded by the source corpus.
People's personal social networks are big and cluttered, and currently there is no good way to automatically organize them. Social networking sites allow users to manually categorize their friends into social circles (e.g., “circles” on Google+, and “lists” 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.
Subfields: Artificial Intelligence, Statistical and Nonlinear Physics
View Paper: https://www.semanticscholar.org/paper/ea3b0cf7578e43a480ff7bf15792c4962cf36e1d
How researchers see this paper
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In Artificial Intelligence
- Rok Sosic
- Jure Leskovec
- William L. Hamilton
- Marinka Zitnik
- Rex Ying
- Stephan Günnemann
- Jiaxuan You
- Philip S. Yu
In Statistical and Nonlinear Physics
- Rok Sosic
- Jure Leskovec
- Jaewon Yang
- Manuel Gomez-Rodriguez
- Bernhard Schölkopf
- Jon M. Kleinberg
- Marinka Zitnik
- Charu C. Aggarwal
Citations in the corpus
- SNAP: A General Purpose Network Analysis and Graph Mining Library (2016, methodology): “SNAP provides efficient implementations of commonly used traditional algorithms for graph and network analysis, as well as recent algorithms that employ machine learning techniques on graph problems, such as community detection [Yang and Leskovec 2013; Yang and Leskovec 2014 III.”