# Network-based link prediction of scientific concepts -- a Science4Cast competition entry

arXiv: https://arxiv.org/abs/2201.07978 · HTML: https://mlsanity.com/paper/2201.07978/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 13 references that are in the map.
- First posted on arXiv 18 Jan 2022.

## Builds on (most cited first, 3 of 3)

- [Link Prediction in Social Networks: the State-of-the-Art](https://mlsanity.com/paper/1411.5118/) (2014)
- [Quantum Link Prediction in Complex Networks](https://mlsanity.com/paper/2112.04768/) (2021)
- [Degree distributions of bipartite networks and their projections](https://mlsanity.com/paper/1802.04953/) (2018)

## 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)

We report on a model built to predict links in a complex network of scientific concepts, in the context of the Science4Cast 2021 competition. We show that the network heavily favours linking nodes of high degree, indicating that new scientific connections are primarily made between popular concepts, which constitutes the main feature of our model. Besides this notion of popularity, we use a measure of similarity between nodes quantified by a normalized count of their common neighbours to improve the model. Finally, we show that the model can be further improved by considering a time-weighted adjacency matrix with both older and newer links having higher impact in the predictions, representing rooted concepts and state of the art research, respectively.