# Improving random walk rankings with feature selection and imputation

arXiv: https://arxiv.org/abs/2111.15635 · HTML: https://mlsanity.com/paper/2111.15635/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 26 references that are in the map.
- First posted on arXiv 29 Nov 2021.

## Builds on (most cited first, 7 of 7)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Fast Graph Representation Learning with PyTorch Geometric](https://mlsanity.com/paper/1903.02428/) (2019)
- [Open Graph Benchmark: Datasets for Machine Learning on Graphs](https://mlsanity.com/paper/2005.00687/) (2020)
- [Link Prediction Based on Graph Neural Networks](https://mlsanity.com/paper/1802.09691/) (2018)
- [Predicting Research Trends with Semantic and Neural Networks with an application in Quantum Physics](https://mlsanity.com/paper/1906.06843/) (2019)
- [Edge Proposal Sets for Link Prediction](https://mlsanity.com/paper/2106.15810/) (2021)

## Built on by (most cited first, 1 of 1)

- [Predicting the Future of AI with AI: High-quality link prediction in an exponentially growing knowledge network](https://mlsanity.com/paper/2210.00881/) (2022)

## 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.