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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.

to orthogonal transformations of the embeddings, which means that the embedding space does not naturally generalize between graphs and can drift during re-training. One notable exception to this trend is the Planetoid-I algorithm introduced by Yang et al. [40], which is an inductive, embedding-based approach to semi-supervised learning.

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