# A Meta-Learning Approach for Graph Representation Learning in Multi-Task Settings

arXiv: https://arxiv.org/abs/2012.06755 · HTML: https://mlsanity.com/paper/2012.06755/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Dec 2020.

## Built on by (most cited first, 8 of 8)

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- [Data-Efficient Brain Connectome Analysis via Multi-Task Meta-Learning](https://mlsanity.com/paper/2206.04486/) (2022)
- [GraphAttacker: A General Multi-Task GraphAttack Framework](https://mlsanity.com/paper/2101.06855/) (2021)
- [Meta-Learning with Graph Neural Networks: Methods and Applications](https://mlsanity.com/paper/2103.00137/) (2021)
- [Graph Neural Network Expressivity and Meta-Learning for Molecular Property Regression](https://mlsanity.com/paper/2209.13410/) (2022)
- [Understanding Optimal Feature Transfer via a Fine-Grained Bias-Variance Analysis](https://mlsanity.com/paper/2404.12481/) (2024)
- [Meta-SimGNN: Adaptive and Robust WiFi Localization Across Dynamic Configurations and Diverse Scenarios](https://mlsanity.com/paper/2511.14076/) (2025)
- [Predicting Viral Rumors and Vulnerable Users for Infodemic Surveillance](https://mlsanity.com/paper/2401.09724/) (2024)

## Abstract (arXiv)

Graph Neural Networks (GNNs) are a framework for graph representation learning, where a model learns to generate low dimensional node embeddings that encapsulate structural and feature-related information. GNNs are usually trained in an end-to-end fashion, leading to highly specialized node embeddings. However, generating node embeddings that can be used to perform multiple tasks (with performance comparable to single-task models) is an open problem. We propose a novel meta-learning strategy capable of producing multi-task node embeddings. Our method avoids the difficulties arising when learning to perform multiple tasks concurrently by, instead, learning to quickly (i.e. with a few steps of gradient descent) adapt to multiple tasks singularly. We show that the embeddings produced by our method can be used to perform multiple tasks with comparable or higher performance than classically trained models. Our method is model-agnostic and task-agnostic, thus applicable to a wide variety of multi-task domains.