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gptkbp:instanceOf
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gptkb:graph_neural_network_algorithm
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gptkbp:aggregationMethods
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gptkb:GCN
gptkb:LSTM
mean
pooling
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|
gptkbp:application
|
graph classification
link prediction
node classification
|
|
gptkbp:citation
|
highly cited
|
|
gptkbp:codeAvailable
|
https://github.com/williamleif/GraphSAGE
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gptkbp:features
|
inductive learning capability
learns aggregation functions
scalable to large graphs
|
|
gptkbp:fullName
|
Graph Sample and AggregatE
|
|
gptkbp:input
|
graph data
|
|
gptkbp:introduced
|
gptkb:Rex_Ying
gptkb:William_L._Hamilton
gptkb:Jure_Leskovec
|
|
gptkbp:introducedIn
|
2017
|
|
gptkbp:notablePublication
|
gptkb:Inductive_Representation_Learning_on_Large_Graphs
https://arxiv.org/abs/1706.02216
|
|
gptkbp:output
|
node embeddings
|
|
gptkbp:publishedIn
|
gptkb:Neural_Information_Processing_Systems_(NeurIPS)_2017
|
|
gptkbp:purpose
|
inductive node representation learning
|
|
gptkbp:relatedTo
|
gptkb:Graph_Convolutional_Networks
gptkb:DeepWalk
gptkb:node2vec
|
|
gptkbp:bfsParent
|
gptkb:GCN
gptkb:Nvidia_cuGraph
|
|
gptkbp:bfsLayer
|
6
|
|
https://www.w3.org/2000/01/rdf-schema#label
|
GraphSAGE
|