---
abstract: 'We consider the task of [*few shot link prediction*]{}, where the goal is to predict missing edges across multiple graphs using only a small sample of known edges. We show that current link prediction methods are generally ill-equipped to handle this task—as they cannot effectively transfer knowledge between graphs in a multi-graph setting and are unable to effectively learn from very sparse data. To address this challenge, we introduce a new gradient-based meta learning framework, [*Meta-Graph*]{}, that leverages higher-order gradients along with a learned graph signature function that conditionally generates a graph neural network initialization. Using a novel set of few shot link prediction benchmarks, we show that [*Meta-Graph*]{} enables not only fast adaptation but also better final convergence and can effectively learn using only a small sample of true edges.'
author:
- |
    Avishek Joey Bose [^1]\
    McGill University, Mila\
    `joey.bose@mail.mcgill.ca`\
    Ankit Jain\
    Uber AI\
    `ankit.jain@uber.com`\
    Piero Molino\
    Uber AI\
    `piero.molino@uber.com`\
    William L. Hamilton\
    McGill University, Mila\
    `wlh@cs.mcgill.ca`\
bibliography:
- 'bibliography.bib'
title: 'Meta-Graph: Few shot Link Prediction via Meta Learning'
---

Acknowledgements {#acknowledgements .unnumbered}
----------------

The authors would like to thank Thang Bui, Maxime Wabartha, Nadeem Ward, Sebastien Lachapelle, and Zhaocheng Zhu for helpful feedback on earlier drafts of this work. In addition, the authors would like to thank the Uber AI team including other interns that helped shape earlier versions of this idea. Joey Bose is supported by the IVADO PhD fellowship and this work was done as part of his internship at Uber AI.

[^1]: Work done during Uber AI Internship.
