Inductive Representation Learning on Large Graphs

TLDR

Node embeddings in large graphs are widely used for tasks such as recommendation and protein function prediction, but existing methods are transductive and cannot generalize to unseen nodes. GraphSAGE is an inductive framework that uses node features to generate embeddings for previously unseen nodes. GraphSAGE learns a neighborhood‑aggregation function that samples and aggregates local node features to produce embeddings. GraphSAGE outperforms baselines on three inductive node‑classification tasks, accurately classifying unseen nodes in citation, Reddit, and protein‑protein interaction graphs.

Abstract

Low-dimensional embeddings of nodes in large graphs have proved extremely useful in a variety of prediction tasks, from content recommendation to identifying protein functions. However, most existing approaches require that all nodes in the graph are present during training of the embeddings; these previous approaches are inherently transductive and do not naturally generalize to unseen nodes. Here we present GraphSAGE, a general, inductive framework that leverages node feature information (e.g., text attributes) to efficiently generate node embeddings for previously unseen data. Instead of training individual embeddings for each node, we learn a function that generates embeddings by sampling and aggregating features from a node's local neighborhood. Our algorithm outperforms strong baselines on three inductive node-classification benchmarks: we classify the category of unseen nodes in evolving information graphs based on citation and Reddit post data, and we show that our algorithm generalizes to completely unseen graphs using a multi-graph dataset of protein-protein interactions.