"I know what you did last summer": query logs and user privacy
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Graph clustering based on structural/attribute similarities
Proceedings of the VLDB Endowment
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
Find me if you can: improving geographical prediction with social and spatial proximity
Proceedings of the 19th international conference on World wide web
Predicting positive and negative links in online social networks
Proceedings of the 19th international conference on World wide web
Mining advisor-advisee relationships from research publication networks
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
You are where you tweet: a content-based approach to geo-locating twitter users
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Classifying latent user attributes in twitter
SMUC '10 Proceedings of the 2nd international workshop on Search and mining user-generated contents
Democrats, republicans and starbucks afficionados: user classification in twitter
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Learning to infer social ties in large networks
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part III
Discriminating gender on Twitter
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Evolution of social-attribute networks: measurements, modeling, and implications using google+
Proceedings of the 2012 ACM conference on Internet measurement conference
Confluence: conformity influence in large social networks
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
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User attributes, such as occupation, education, and location, are important for many applications. In this paper, we study the problem of profiling user attributes in social network. To capture the correlation between attributes and social connections, we present a new insight that social connections are discriminatively correlated with attributes via a hidden factor -- relationship type. For example, a user's colleagues are more likely to share the same employer with him than other friends. Based on the insight, we propose to co-profile users' attributes and relationship types of their connections. To achieve co-profiling, we develop an efficient algorithm based on an optimization framework. Our algorithm captures our insight effectively. It iteratively profiles attributes by propagation via certain types of connections, and profiles types of connections based on attributes and the network structure. We conduct extensive experiments to evaluate our algorithm. The results show that our algorithm profiles various attributes accurately, which improves the state-of-the-art methods by 12%.