Graph Meta Learning via Subgraphs

Published: Jun 15, 2020

G-Meta is a meta-learning approach for graphs that quickly adapts to new prediction tasks using only a handful of data points. G-Meta works in most challenging, few-shot learning settings and scales to massive interactomics data as we show on our new Networks of Life dataset comprising of 1,840 networks.

Zitnik Lab  ·  Harvard  ·  Department of Biomedical Informatics