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Poster
Inductive Representation Learning on Large Graphs
Will Hamilton · Zhitao Ying · Jure Leskovec

Mon Dec 04 06:30 PM -- 10:30 PM (PST) @ Pacific Ballroom #71 #None

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. 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.

Author Information

Will Hamilton (Stanford University)
Rex Ying (Stanford University)
Jure Leskovec (Stanford University and Pinterest)

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