Ranking Researchers Through Collaboration Pattern Analysis
Abstract
The academic world utterly relies on the concept of scientific collaboration. As in every collaborative network, however, the production of research articles follows hidden co-authoring principles as well as temporal dynamics which generate latent and complex collaboration patterns. In this paper, we present an online advanced tool for real-time rankings of computer scientists under these perspectives.
Cite
Text
Cataldi et al. "Ranking Researchers Through Collaboration Pattern Analysis." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2016. doi:10.1007/978-3-319-46131-1_11Markdown
[Cataldi et al. "Ranking Researchers Through Collaboration Pattern Analysis." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2016.](https://mlanthology.org/ecmlpkdd/2016/cataldi2016ecmlpkdd-ranking/) doi:10.1007/978-3-319-46131-1_11BibTeX
@inproceedings{cataldi2016ecmlpkdd-ranking,
title = {{Ranking Researchers Through Collaboration Pattern Analysis}},
author = {Cataldi, Mario and Di Caro, Luigi and Schifanella, Claudio},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2016},
pages = {50-54},
doi = {10.1007/978-3-319-46131-1_11},
url = {https://mlanthology.org/ecmlpkdd/2016/cataldi2016ecmlpkdd-ranking/}
}