On Augmenting the References Section with a Citation Network Visualization
Abstract
Researchers often find relevant articles by looking at the references section. We conducted user interviews with researchers about their workflow and their needs when carrying out literature research. Based on this study, we identify a set of problems encountered by researchers. We then propose to embed classified citation networks into articles as a solution, and to complement this graph with optional comments about references by authors. We demonstrate this idea by implementing it for this article. We argue that our solution helps increase inclusivity and improves the efficiency of reading scientific articles.
Cite
Text
Manggala et al. "On Augmenting the References Section with a Citation Network Visualization." ICLR 2021 Workshops: Rethinking_ML_Papers, 2021.Markdown
[Manggala et al. "On Augmenting the References Section with a Citation Network Visualization." ICLR 2021 Workshops: Rethinking_ML_Papers, 2021.](https://mlanthology.org/iclrw/2021/manggala2021iclrw-augmenting/)BibTeX
@inproceedings{manggala2021iclrw-augmenting,
title = {{On Augmenting the References Section with a Citation Network Visualization}},
author = {Manggala, Putra and Atoyan, Tigran and Samosir, Gracia and Varsava, Jan and Ruf, Johannes},
booktitle = {ICLR 2021 Workshops: Rethinking_ML_Papers},
year = {2021},
url = {https://mlanthology.org/iclrw/2021/manggala2021iclrw-augmenting/}
}