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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.

node classification, clustering, and link prediction [11, 28, 35]. ... (e.g., citation data with text attributes, biological data with functional/molecular markers), our approach can also make use of structural features that are present in all graphs (e.g., node degrees). ... through theoretical analysis, that GraphSAGE is capable of learning ...

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