{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/inductive-representation-learning-on-large","title":"Inductive Representation Learning on Large Graphs","arxiv_id":"1706.02216","date":"2017-06-07","proceeding":"NeurIPS 2017 12","authors":["William L. Hamilton","Rex Ying","Jure Leskovec"],"abstract":"Low-dimensional embeddings of nodes in large graphs have proved extremely\nuseful in a variety of prediction tasks, from content recommendation to\nidentifying protein functions. However, most existing approaches require that\nall nodes in the graph are present during training of the embeddings; these\nprevious approaches are inherently transductive and do not naturally generalize\nto unseen nodes. Here we present GraphSAGE, a general, inductive framework that\nleverages node feature information (e.g., text attributes) to efficiently\ngenerate node embeddings for previously unseen data. Instead of training\nindividual embeddings for each node, we learn a function that generates\nembeddings by sampling and aggregating features from a node's local\nneighborhood. Our algorithm outperforms strong baselines on three inductive\nnode-classification benchmarks: we classify the category of unseen nodes in\nevolving information graphs based on citation and Reddit post data, and we show\nthat our algorithm generalizes to completely unseen graphs using a multi-graph\ndataset of protein-protein interactions.","url_abs":"http://arxiv.org/abs/1706.02216v4","url_pdf":"http://arxiv.org/pdf/1706.02216v4.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 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