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Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence Context Sensitive Topic Models for Author Influence in Document Networks
BibTeX
@MISC{Kataria_proceedingsof,
author = {Saurabh Kataria and Prasenjit Mitra and Cornelia Caragea and C. Lee Giles},
title = {Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence Context Sensitive Topic Models for Author Influence in Document Networks},
year = {}
}
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Abstract
In a document network such as a citation network of scientific documents, web-logs, etc., the content produced by authors exhibits their interest in certain topics. In addition some authors influence other authors ’ interests. In this work, we propose to model the influence of cited authors along with the interests of citing authors. Moreover, we hypothesize that apart from the citations present in documents, the context surrounding the citation mention provides extra topical information about the cited authors. However, associating terms in the context to the cited authors remains an open problem. We propose novel document generation schemes that incorporate the context while simultaneously modeling the interests of citing authors and influence of the cited authors. Our experiments show significant improvements over baseline models for various evaluation criteria such as link prediction between document and cited author, and quantitatively explaining unseen text. 1
Keyphrases
document network author influence cited author twenty-second international joint conference baseline model citation network significant improvement unseen text citation mention certain topic novel document generation extra topical information scientific document various evaluation criterion open problem link prediction author interest
