Uncertainty in Artificial Intelligence
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Modeling Citation Networks Using Latent Random Offsets
Willie Neiswanger, Chong Wang, Qirong Ho, Eric Xing
Out of the many potential factors that deter- mine which links form in a document citation network, two in particular are of high impor- tance: first, a document may be cited based on its subject matterā??this can be modeled by analyzing document content; second, a doc- ument may be cited based on which other documents have previously cited itā??this can be modeled by analyzing citation structure. Both factors are important for users to make informed decisions and choose appropriate ci- tations 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 demon- strate 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.
Pages: 633-642
PS Link:
PDF Link: /papers/14/p633-neiswanger.pdf
AUTHOR = "Willie Neiswanger and Chong Wang and Qirong Ho and Eric Xing",
TITLE = "Modeling Citation Networks Using Latent Random Offsets",
BOOKTITLE = "Proceedings of the Thirtieth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-14)",
ADDRESS = "Corvallis, Oregon",
YEAR = "2014",
PAGES = "633--642"

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