# Link prediction with continuous-time classical and quantum walks

arXiv: https://arxiv.org/abs/2208.11030 · HTML: https://mlsanity.com/paper/2208.11030/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 66 references that are in the map.
- First posted on arXiv 23 Aug 2022.

## Builds on (most cited first, 4 of 4)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Random walks and diffusion on networks](https://mlsanity.com/paper/1612.03281/) (2016)
- [Progresses and Challenges in Link Prediction](https://mlsanity.com/paper/2102.11472/) (2021)
- [Quantum Link Prediction in Complex Networks](https://mlsanity.com/paper/2112.04768/) (2021)

## Built on by (most cited first, 2 of 2)

- [Disease Gene Prioritization With Quantum Walks](https://mlsanity.com/paper/2311.05486/) (2023)
- [CTQWformer: A CTQW-based Transformer for Graph Classification](https://mlsanity.com/paper/2605.09486/) (2026)

## Abstract (arXiv)

Protein-protein interaction (PPI) networks consist of the physical and/or functional interactions between the proteins of an organism. Since the biophysical and high-throughput methods used to form PPI networks are expensive, time-consuming, and often contain inaccuracies, the resulting networks are usually incomplete. In order to infer missing interactions in these networks, we propose a novel class of link prediction methods based on continuous-time classical and quantum random walks. In the case of quantum walks, we examine the usage of both the network adjacency and Laplacian matrices for controlling the walk dynamics. We define a score function based on the corresponding transition probabilities and perform tests on four real-world PPI datasets. Our results show that continuous-time classical random walks and quantum walks using the network adjacency matrix can successfully predict missing protein-protein interactions, with performance rivalling the state of the art.