Unsupervised learning by probabilistic latent semantic analysis
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Quality of trust for social trust path selection in complex social networks
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A Combination Approach to Web User Profiling
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Modeling Social Annotation: A Bayesian Approach
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Trend mining in social networks: a study using a large cattle movement database
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Superficial method for extracting social network for academics using web snippets
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Groups without tears: mining social topologies from email
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Information at your fingertips: contextual IR in enterprise email
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WI-IAT '11 Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 03
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Effective email network visualization techniques by means of user behaviors
Intelligent Data Analysis
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Previous work in social network analysis (SNA) has modeled the existence of links from one entity to another, but not the attributes such as language content or topics on those links. We present the Author-Recipient-Topic (ART) model for social network analysis, which learns topic distributions based on the direction-sensitive messages sent between entities. The model builds on Latent Dirichlet Allocation (LDA) and the Author-Topic (AT) model, adding the key attribute that distribution over topics is conditioned distinctly on both the sender and recipient--steering the discovery of topics according to the relationships between people. We give results on both the Enron email corpus and a researcher's email archive, providing evidence not only that clearly relevant topics are discovered, but that the ART model better predicts people's roles and gives lower perplexity on previously unseen messages. We also present the Role-Author-Recipient-Topic (RART) model, an extension to ART that explicitly represents people's roles.