Improving random walk rankings with feature selection and imputation
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Abstract (arXiv)
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, ee5a, achieved a score of 0.92738 on the test set. Our team's score ranks 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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 7 of its 26 references that are in the map. First posted on arXiv 29 Nov 2021.
Builds on
7 of 26 references.
References · 7
- 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
- 2017Inductive Representation Learning on Large Graphscited by 6,284
- 2019Fast Graph Representation Learning with PyTorch Geometriccited by 2,414
- 2020Open Graph Benchmark: Datasets for Machine Learning on Graphscited by 2,148
- 2018Link Prediction Based on Graph Neural Networkscited by 829
- 2019Predicting Research Trends with Semantic and Neural Networks with an application in Quantum Physicscited by 24
- 2021Edge Proposal Sets for Link Predictioncited by 16
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Citing papers per year: 2022: 1.
1 paper.