Graph Meta Learning via Local Subgraphs

Prevailing methods for graphs require abundant label and edge information for learning. When data for a new task are scarce, meta learning can learn from prior experiences and form much-needed inductive biases for fast adaption to new tasks. Here, we introduce G-META, a novel meta-learning algorithm for graphs. G-META uses local subgraphs to transfer subgraph-specific information and learn transferable knowledge faster via meta gradients. G-META learns how to quickly adapt to a new task using only a handful of nodes or edges in the new task and does so by learning from data points in other graphs or related, albeit disjoint, label sets. G-META is theoretically justified as we show that the evidence for a prediction can be found in the local subgraph surrounding the target node or edge. Experiments on seven datasets and nine baseline methods show that G-META outperforms existing methods by up to 16.3%. Unlike previous methods, G-META successfully learns in challenging, few-shot learning settings that require generalization to completely new graphs and never-before-seen labels. Finally, G-META scales to large graphs, which we demonstrate on a new Tree-of-Life dataset comprising 1,840 graphs, a two-orders of magnitude increase in the number of graphs used in prior work. 34th Conference on Neural Information Processing Systems (NeurIPS 2020),