Improving random walk rankings with feature selection and imputation

Ngoc Mai Tran; Yangxinyu Xie

doi:10.1109/bigdata52589.2021.9671785

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arXiv · 2111.15635

Improving random walk rankings with feature selection and imputation

Ngoc Mai Tran·Yangxinyu Xie

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Abstract

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.

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Ngoc Mai Tran, Yangxinyu Xie. 2021-11-29. Improving random walk rankings with feature selection and imputation. https://doi.org/10.1109/bigdata52589.2021.9671785

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