Bibliographic Analysis with the Citation Network Topic Model
Digital Libraries
2016-09-23 v1 Machine Learning
Machine Learning
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 a clustering task compared to several baselines.
Cite
@article{arxiv.1609.06826,
title = {Bibliographic Analysis with the Citation Network Topic Model},
author = {Kar Wai Lim and Wray Buntine},
journal= {arXiv preprint arXiv:1609.06826},
year = {2016}
}
Comments
A copy of ACML paper. arXiv admin note: substantial text overlap with arXiv:1609.06532