Bibliographic Analysis with the Citation Network Topic Model

Kar Wai Lim, Wray Buntine
Proceedings of the Sixth Asian Conference on Machine Learning, PMLR 39:142-158, 2015.

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 this Paper

BibTeX
@InProceedings{pmlr-v39-lim14, 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}, pages = {142--158}, year = {2015}, editor = {Phung, Dinh and Li, Hang}, volume = {39}, series = {Proceedings of Machine Learning Research}, address = {Nha Trang City, Vietnam}, month = {26--28 Nov}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v39/lim14.pdf}, url = {https://proceedings.mlr.press/v39/lim14.html}, 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.} }
Endnote
%0 Conference Paper %T Bibliographic Analysis with the Citation Network Topic Model %A Kar Wai Lim %A Wray Buntine %B Proceedings of the Sixth Asian Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2015 %E Dinh Phung %E Hang Li %F pmlr-v39-lim14 %I PMLR %P 142--158 %U https://proceedings.mlr.press/v39/lim14.html %V 39 %X 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.
RIS
TY - CPAPER TI - Bibliographic Analysis with the Citation Network Topic Model AU - Kar Wai Lim AU - Wray Buntine BT - Proceedings of the Sixth Asian Conference on Machine Learning DA - 2015/02/16 ED - Dinh Phung ED - Hang Li ID - pmlr-v39-lim14 PB - PMLR DP - Proceedings of Machine Learning Research VL - 39 SP - 142 EP - 158 L1 - http://proceedings.mlr.press/v39/lim14.pdf UR - https://proceedings.mlr.press/v39/lim14.html AB - 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. ER -
APA
Lim, K.W. & Buntine, W.. (2015). Bibliographic Analysis with the Citation Network Topic Model. Proceedings of the Sixth Asian Conference on Machine Learning, in Proceedings of Machine Learning Research 39:142-158 Available from https://proceedings.mlr.press/v39/lim14.html.

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