Inductive Representation Learning on Large Graphs

Inductive Representation Learning on Large GraphsWilliam L. Hamilton Ying of Computer ScienceStanford UniversityStanford, CA, 94305 AbstractLow-dimensional embeddings of nodes in Large Graphs have proved extremelyuseful in a variety of prediction tasks, from content recommendation to identifyingprotein functions. However, most existing approaches require that all nodes in thegraph are present during training of the embeddings; these previous approaches areinherentlytransductiveand do not naturally generalize to unseen nodes. Here wepresent GraphSAGE, a generalinductiveframework that leverages node featureinformation ( , text attributes) to efficiently generate node embeddings forpreviously unseen data. Instead of training individual embeddings for each node,we learn a function that generates embeddings by sampling and aggregating featuresfrom a node s local neighborhood.

Inductive Representation Learning on Large Graphs William L. Hamilton wleif@stanford.edu Rex Ying rexying@stanford.edu Jure Leskovec jure@cs.stanford.edu Department of Computer Science Stanford University Stanford, CA, 94305 Abstract Low-dimensional embeddings of nodes in large graphs have proved extremely

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  Large, Learning, Representation, Inductive, Graph, Inductive representation learning on large graphs, Large graphs

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