Context Sensitive Topic Models for Author Influence in Document Networks
Saurabh Kataria, Prasenjit Mitra, Cornelia Caragea and C. Lee Giles
In a document network such as citation network of scientific documents, web-logs, etc., the content produced by authors exhibit their \textit{interest} in certain \textit{topics} whereas some authors tend to \textit{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 a 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 remain an open problem. We propose a novel document generation schemes that incorporate the context while modeling the interests of citing authors and influence of the cited authors simultaneously. Our experiments show significant improvements over baseline models for various evaluation criteria such as link prediction between document and cited author, log-likelihood estimation on unseen text.