The Science4cast Competition consists of predicting new links in a semantic network, with each node representing a concept and each edge representing a link proposed by a paper relating two concepts. This network contains information from 1994-2017, with a discretization of days (which represents the publication date of the underlying papers). Team Hash Brown's final submission, \emph{ee5a}, achieved a score of 0.92738 on the test set. Our team's score ranks \emph{second place}, 0.01 below the winner's score. This paper details our model, its intuition, and the performance of its variations in the test set.
Cite
@article{arxiv.2111.15635,
title = {Improving random walk rankings with feature selection and imputation},
author = {Ngoc Mai Tran and Yangxinyu Xie},
journal= {arXiv preprint arXiv:2111.15635},
year = {2022}
}