Modeling Citation Networks using Latent Random Offsets

Willie Neiswanger Carnegie Mellon University, Chong Wang, Qirong Ho Carnegie Mellon University, Eric Xing Carnegie Mellon University
Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence, PMLR R12:638-647, 2014.

Abstract

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 matter—this can be modeled by analyzing document content; second, a doc- ument 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 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.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR12-university14q, title = {Modeling Citation Networks using Latent Random Offsets}, author = {University, Willie Neiswanger Carnegie Mellon and Wang, Chong and University, Qirong Ho Carnegie Mellon and University, Eric Xing Carnegie Mellon}, booktitle = {Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence}, pages = {638--647}, year = {2014}, editor = {Zhang, Nevin L. and Tian, Jin}, volume = {R12}, series = {Proceedings of Machine Learning Research}, month = {23--27 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r12/main/assets/university14q/university14q.pdf}, url = {https://proceedings.mlr.press/r12/university14q.html}, abstract = {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 matter—this can be modeled by analyzing document content; second, a doc- ument 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 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.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Modeling Citation Networks using Latent Random Offsets %A Willie Neiswanger Carnegie Mellon University %A Chong Wang %A Qirong Ho Carnegie Mellon University %A Eric Xing Carnegie Mellon University %B Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2014 %E Nevin L. Zhang %E Jin Tian %F pmlr-vR12-university14q %I PMLR %P 638--647 %U https://proceedings.mlr.press/r12/university14q.html %V R12 %X 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 matter—this can be modeled by analyzing document content; second, a doc- ument 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 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. %Z Reissued by PMLR on 04 October 2026.
APA
University, W.N.C.M., Wang, C., University, Q.H.C.M. & University, E.X.C.M.. (2014). Modeling Citation Networks using Latent Random Offsets. Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R12:638-647 Available from https://proceedings.mlr.press/r12/university14q.html. Reissued by PMLR on 04 October 2026.

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