Discrete Applied Mathematics
Information Theory, Inference & Learning Algorithms
Information Theory, Inference & Learning Algorithms
Effciently Solving Dynamic Markov Random Fields Using Graph Cuts
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Proceedings of the 15th international conference on World Wide Web
Less is more: probabilistic models for retrieving fewer relevant documents
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Minimizing Nonsubmodular Functions with Graph Cuts-A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mixed Membership Stochastic Blockmodels
The Journal of Machine Learning Research
Topic-link LDA: joint models of topic and author community
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Connections between the lines: augmenting social networks with text
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Turning down the noise in the blogosphere
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
You are who you know: inferring user profiles in online social networks
Proceedings of the third ACM international conference on Web search and data mining
Empirical comparison of algorithms for network community detection
Proceedings of the 19th international conference on World wide web
Toward Finding Hidden Communities Based on User Profile
ICDMW '10 Proceedings of the 2010 IEEE International Conference on Data Mining Workshops
A Log-Linear Model with Latent Features for Dyadic Prediction
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
Who says what to whom on twitter
Proceedings of the 20th international conference on World wide web
Link prediction via matrix factorization
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part II
Tadvise: a twitter assistant based on twitter lists
SocInfo'11 Proceedings of the Third international conference on Social informatics
Multi-assignment clustering for boolean data
The Journal of Machine Learning Research
Community-Affiliation Graph Model for Overlapping Network Community Detection
ICDM '12 Proceedings of the 2012 IEEE 12th International Conference on Data Mining
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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.