# Explainable Link Prediction for Privacy-Preserving Contact Tracing

arXiv: https://arxiv.org/abs/2012.05516 · HTML: https://mlsanity.com/paper/2012.05516/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Dec 2020.

## Built on by (most cited first, 4 of 4)

- [Similar Cases Recommendation using Legal Knowledge Graphs](https://mlsanity.com/paper/2107.04771/) (2021)
- [Towards Automated Evaluation of Explanations in Graph Neural Networks](https://mlsanity.com/paper/2106.11864/) (2021)
- [Reimagining GNN Explanations with ideas from Tabular Data](https://mlsanity.com/paper/2106.12665/) (2021)
- [xEM: Explainable Entity Matching in Customer 360](https://mlsanity.com/paper/2212.00342/) (2022)

## Abstract (arXiv)

Contact Tracing has been used to identify people who were in close proximity to those infected with SARS-Cov2 coronavirus. A number of digital contract tracing applications have been introduced to facilitate or complement physical contact tracing. However, there are a number of privacy issues in the implementation of contract tracing applications, which make people reluctant to install or update their infection status on these applications. In this concept paper, we present ideas from Graph Neural Networks and explainability, that could improve trust in these applications, and encourage adoption by people.