Improving random walk rankings with feature selection and imputation
This is an incremental improvement in link prediction for semantic networks in a specific competition context.
The paper tackled the problem of predicting new links in a semantic network for the Science4cast Competition, achieving a score of 0.92738 and ranking second place, 0.01 below the winner.
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.