Scientific Ranking over Heterogeneous Academic Hypernetwork
Abstract
Ranking is an important way of retrieving authoritative papers from a large scientific literature database. Current state-of-the-art exploits the flat structure of the heterogeneous academic network to achieve a better ranking of scientific articles, however, ignores the multinomial nature of the multidimensional relationships between different types of academic entities. This paper proposes a novel mutual ranking algorithm based on the multinomial heterogeneous academic hypernetwork, which serves as a generalized model of a scientific literature database. The proposed algorithm is demonstrated effective through extensive evaluation against well-known IR metrics on a well-established benchmarking environment based on the ACL Anthology Network.
Cite
Text
Liang and Jiang. "Scientific Ranking over Heterogeneous Academic Hypernetwork." AAAI Conference on Artificial Intelligence, 2016. doi:10.1609/AAAI.V30I1.10004Markdown
[Liang and Jiang. "Scientific Ranking over Heterogeneous Academic Hypernetwork." AAAI Conference on Artificial Intelligence, 2016.](https://mlanthology.org/aaai/2016/liang2016aaai-scientific/) doi:10.1609/AAAI.V30I1.10004BibTeX
@inproceedings{liang2016aaai-scientific,
title = {{Scientific Ranking over Heterogeneous Academic Hypernetwork}},
author = {Liang, Ronghua and Jiang, Xiaorui},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2016},
pages = {20-26},
doi = {10.1609/AAAI.V30I1.10004},
url = {https://mlanthology.org/aaai/2016/liang2016aaai-scientific/}
}