Inductive Representation Learning on Large Graphs
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.
PDF Abstract NeurIPS 2017 PDF NeurIPS 2017 AbstractCode
Datasets
Introduced in the Paper:
Reddit
PPI
Used in the Paper:
CIFAR-10
Pubmed
OGB
Flickr30k
Cora
Citeseer
ZINC
Squirrel (60%/20%/20% random splits)
Wisconsin(60%/20%/20% random splits)
Film (60%/20%/20% random splits)
PubMed (60%/20%/20% random splits)
Cornell (60%/20%/20% random splits)
Texas(60%/20%/20% random splits)
Chameleon(60%/20%/20% random splits)
USA Air-Traffic
Brazil Air-Traffic
