Date of Original Version
Abstract or Description
Out of the many potential factors that determine which links form in a document citation network, two in particular are of high importance: first, a document may be cited based on its subject matter—this can be modeled by analyzing document content; second, a document may be cited based on which other documents have previously cited it—this can be modeled by analyzing citation structure. Both factors are important for users to make informed decisions and choose appropriate citations as the network grows. In this paper, we present a novel model that integrates the merits of content and citation analyses into a single probabilistic framework. We demonstrate our model on three real-world citation networks. Compared with existing baselines, our model can be used to effectively explore a citation network and provide meaningful explanations for links while still maintaining competitive citation prediction performance.
Proceedings of the 30th International Conference on Conference on Uncertainty in Artificial Intelligence (UAI 2014).