# Few-shot link prediction via graph neural networks for Covid-19 drug-repurposing

arXiv: https://arxiv.org/abs/2007.10261 · HTML: https://mlsanity.com/paper/2007.10261/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jul 2020.

## Built on by (most cited first, 22 of 22)

- [Neural Bellman-Ford Networks: A General Graph Neural Network Framework for Link Prediction](https://mlsanity.com/paper/2106.06935/) (2021)
- [Automated Machine Learning on Graphs: A Survey](https://mlsanity.com/paper/2103.00742/) (2021)
- [A Review of Biomedical Datasets Relating to Drug Discovery: A Knowledge Graph Perspective](https://mlsanity.com/paper/2102.10062/) (2021)
- [PaGE-Link: Path-based Graph Neural Network Explanation for Heterogeneous Link Prediction](https://mlsanity.com/paper/2302.12465/) (2023)
- [On Strengthening and Defending Graph Reconstruction Attack with Markov Chain Approximation](https://mlsanity.com/paper/2306.09104/) (2023)
- [Automated Graph Machine Learning: Approaches, Libraries, Benchmarks and Directions](https://mlsanity.com/paper/2201.01288/) (2022)
- [Efficient and effective training of language and graph neural network models](https://mlsanity.com/paper/2206.10781/) (2022)
- [Combating Bilateral Edge Noise for Robust Link Prediction](https://mlsanity.com/paper/2311.01196/) (2023)
- [Optimizing Long-tailed Link Prediction in Graph Neural Networks through Structure Representation Enhancement](https://mlsanity.com/paper/2407.20499/) (2024)
- [KGML-xDTD: A Knowledge Graph-based Machine Learning Framework for Drug Treatment Prediction and Mechanism Description](https://mlsanity.com/paper/2212.01384/) (2022)
- [Beyond Transduction: A Survey on Inductive, Few Shot, and Zero Shot Link Prediction in Knowledge Graphs](https://mlsanity.com/paper/2312.04997/) (2023)
- [Analysis of Drug repurposing Knowledge graphs for Covid-19](https://mlsanity.com/paper/2212.03911/) (2022)
- [A clustering and graph deep learning-based framework for COVID-19 drug repurposing](https://mlsanity.com/paper/2306.13995/) (2023)
- [Learning Representations for Reasoning: Generalizing Across Diverse Structures](https://mlsanity.com/paper/2410.13018/) (2024)
- [Integrating Social Determinants of Health into Knowledge Graphs: Evaluating Prediction Bias and Fairness in Healthcare](https://mlsanity.com/paper/2412.00245/) (2024)
- [DKG-LLM : A Framework for Medical Diagnosis and Personalized Treatment Recommendations via Dynamic Knowledge Graph and Large Language Model Integration](https://mlsanity.com/paper/2508.06186/) (2025)
- [Unifying biomedical knowledge in a modern multimodal graph](https://mlsanity.com/paper/2604.27269/) (2026)
- [Not All Negatives Are Worth Attending to: Meta-Bootstrapping Negative Sampling Framework for Link Prediction](https://mlsanity.com/paper/2312.04815/) (2023)
- [SKG-LLM: Developing a Mathematical Model for Stroke Knowledge Graph Construction Using Large Language Models](https://mlsanity.com/paper/2503.06475/) (2025)
- [VitaGraph: Building a Knowledge Graph for Biologically Relevant Learning Tasks](https://mlsanity.com/paper/2505.11185/) (2025)
- [A Community-Enhanced Graph Representation Model for Link Prediction](https://mlsanity.com/paper/2512.21166/) (2025)
- [Beyond Homophily: Towards Generalized Graph Reconstruction Attack and Defense](https://mlsanity.com/paper/2606.08067/) (2026)

## Abstract (arXiv)

Predicting interactions among heterogenous graph structured data has numerous applications such as knowledge graph completion, recommendation systems and drug discovery. Often times, the links to be predicted belong to rare types such as the case in repurposing drugs for novel diseases. This motivates the task of few-shot link prediction. Typically, GCNs are ill-equipped in learning such rare link types since the relation embedding is not learned in an inductive fashion. This paper proposes an inductive RGCN for learning informative relation embeddings even in the few-shot learning regime. The proposed inductive model significantly outperforms the RGCN and state-of-the-art KGE models in few-shot learning tasks. Furthermore, we apply our method on the drug-repurposing knowledge graph (DRKG) for discovering drugs for Covid-19. We pose the drug discovery task as link prediction and learn embeddings for the biological entities that partake in the DRKG. Our initial results corroborate that several drugs used in clinical trials were identified as possible drug candidates. The method in this paper are implemented using the efficient deep graph learning (DGL)