Unsupervised Prediction of Citation Influences
Abstract
Publication repositories contain an abundance of information about the evolution of scientific research areas. We address the problem of creating a visualization of a research area that describes the flow of topics between papers, quantifies the impact that papers have on each other, and helps to identify key contributions. To this end, we devise a probabilistic topic model that explains the generation of documents; the model incorporates the aspects of topical innovation and topical inheritance via citations. We evaluate the model's ability to predict the strength of influence of citations against manually rated citations.
Cite
Text
Dietz et al. "Unsupervised Prediction of Citation Influences." International Conference on Machine Learning, 2007. doi:10.1145/1273496.1273526Markdown
[Dietz et al. "Unsupervised Prediction of Citation Influences." International Conference on Machine Learning, 2007.](https://mlanthology.org/icml/2007/dietz2007icml-unsupervised/) doi:10.1145/1273496.1273526BibTeX
@inproceedings{dietz2007icml-unsupervised,
title = {{Unsupervised Prediction of Citation Influences}},
author = {Dietz, Laura and Bickel, Steffen and Scheffer, Tobias},
booktitle = {International Conference on Machine Learning},
year = {2007},
pages = {233-240},
doi = {10.1145/1273496.1273526},
url = {https://mlanthology.org/icml/2007/dietz2007icml-unsupervised/}
}