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gptkbp:instanceOf
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graph neural network model
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gptkbp:abbreviation
|
gptkb:SGC
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gptkbp:advantage
|
lower memory usage
competitive accuracy
faster than standard GCN
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|
gptkbp:application
|
semi-supervised learning
graph representation learning
node classification
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gptkbp:arXivID
|
1902.07153
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gptkbp:basedOn
|
gptkb:Graph_Convolutional_Network
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gptkbp:benchmarkDataset
|
gptkb:Cora
gptkb:Citeseer
Pubmed
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gptkbp:citation
|
gptkb:Simplifying_Graph_Convolutional_Networks
10.48550/arXiv.1902.07153
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gptkbp:contribution
|
enables faster training
improves efficiency of GCNs
reduces overfitting
removes nonlinearities and collapses weight matrices between GCN layers
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gptkbp:fullName
|
gptkb:Simplified_Graph_Convolutional_Network
|
|
https://www.w3.org/2000/01/rdf-schema#label
|
Simplified GCN
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|
gptkbp:input
|
graph
adjacency matrix
node features
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|
gptkbp:key
|
precompute K-step feature propagation
use a single linear classifier after propagation
|
|
gptkbp:limitation
|
cannot model complex nonlinear relationships
less expressive than deep GCNs
|
|
gptkbp:output
|
node labels
node embeddings
|
|
gptkbp:proposedBy
|
gptkb:Zhiwei_Steven_Wu
gptkb:Tianyi_Zhang
gptkb:Felix_Wu
gptkb:Amauri_Holanda_de_Souza_Jr.
gptkb:Khaled_S._Refaat
gptkb:Kirill_Levchenko
gptkb:Tianjian_Meng
Christopher Fifty
|
|
gptkbp:publicationYear
|
2019
|
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gptkbp:publishedIn
|
gptkb:ICML_2019
|
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gptkbp:relatedTo
|
gptkb:GCN
gptkb:convolutional_neural_network
gptkb:Graph_Convolutional_Network
|
|
gptkbp:repository
|
https://github.com/Tiiiger/SGC
|
|
gptkbp:usedIn
|
social networks
biological networks
citation networks
|
|
gptkbp:bfsParent
|
gptkb:GCN
|
|
gptkbp:bfsLayer
|
6
|