Bibliographic Analysis with the Citation Network Topic Model
Abstract
Bibliographic analysis considers author’s research areas, the citation network and paper content among other things. In this paper, we combine these three in a topic model that produces a bibliographic model of authors, topics and documents using a non-parametric extension of a combination of the Poisson mixed-topic link model and the author-topic model. We propose a novel and efficient inference algorithm for the model to explore subsets of research publications from CiteSeerX. Our model demonstrates improved performance in both model fitting and clustering task comparing to several baselines.
Cite
Text
Lim and Buntine. "Bibliographic Analysis with the Citation Network Topic Model." Proceedings of the Sixth Asian Conference on Machine Learning, 2014.Markdown
[Lim and Buntine. "Bibliographic Analysis with the Citation Network Topic Model." Proceedings of the Sixth Asian Conference on Machine Learning, 2014.](https://mlanthology.org/acml/2014/lim2014acml-bibliographic/)BibTeX
@inproceedings{lim2014acml-bibliographic,
title = {{Bibliographic Analysis with the Citation Network Topic Model}},
author = {Lim, Kar Wai and Buntine, Wray},
booktitle = {Proceedings of the Sixth Asian Conference on Machine Learning},
year = {2014},
pages = {142-158},
volume = {39},
url = {https://mlanthology.org/acml/2014/lim2014acml-bibliographic/}
}