Date:2018-09-10
Author:William L. Hamilton*, Rex Ying*, Jure Leskovec
Pages:19
Summary:The paper introduces GraphSAGE, a general inductive framework for generating node embeddings in large graphs. Unlike existing methods that require all nodes to be present during training, GraphSAGE leverages node feature information to efficiently generate embeddings for unseen nodes. The algorithm learns a set of aggregator functions that aggregate feature information from a node's local neighborhood, allowing it to generalize to new graphs and unseen nodes. The authors evaluate GraphSAGE on three benchmarks: classifying academic papers, Reddit posts, and protein functions, demonstrating significant improvements over baselines in terms of classification accuracy and runtime efficiency. The paper also discusses the theoretical underpinnings of GraphSAGE, showing that it can approximate clustering coefficients and learn about local graph structures even when node features are sampled from a continuous distribution.