W. Hamilton, R. Ying, and J. Leskovec. (2017)cite arxiv:1706.02216Comment: Published in NIPS 2017; version with full appendix and minor corrections.
Abstract
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 for previously unseen data. 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.
Description
[1706.02216] Inductive Representation Learning on Large Graphs
%0 Generic
%1 hamilton2017inductive
%A Hamilton, William L.
%A Ying, Rex
%A Leskovec, Jure
%D 2017
%K graph-based
%T Inductive Representation Learning on Large Graphs
%U http://arxiv.org/abs/1706.02216
%X 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 for previously unseen data. 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.
@misc{hamilton2017inductive,
abstract = {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 for previously unseen data. 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.},
added-at = {2019-06-11T09:50:43.000+0200},
author = {Hamilton, William L. and Ying, Rex and Leskovec, Jure},
biburl = {https://www.bibsonomy.org/bibtex/2f496be9f6d8f8db9d173a177385fa520/e.fischer},
description = {[1706.02216] Inductive Representation Learning on Large Graphs},
interhash = {c1c7b27050f39aaec632fbbb2d3a6810},
intrahash = {f496be9f6d8f8db9d173a177385fa520},
keywords = {graph-based},
note = {cite arxiv:1706.02216Comment: Published in NIPS 2017; version with full appendix and minor corrections},
timestamp = {2019-06-11T09:50:43.000+0200},
title = {Inductive Representation Learning on Large Graphs},
url = {http://arxiv.org/abs/1706.02216},
year = 2017
}