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gptkbp:instanceOf
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gptkb:convolutional_neural_network
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gptkbp:abbreviation
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gptkb:GAT
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gptkbp:activatedBy
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gptkb:ELU
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gptkbp:application
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graph classification
link prediction
node classification
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gptkbp:architecture
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multi-head attention
layer stacking
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gptkbp:attentionMechanism
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learns weights for neighbors
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gptkbp:benchmarkDatasets
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gptkb:Cora
gptkb:Citeseer
Pubmed
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gptkbp:category
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gptkb:artificial_intelligence
graph representation learning
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gptkbp:citation
|
over 10,000 (as of 2024)
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gptkbp:codeAvailable
|
https://github.com/PetarV-/GAT
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gptkbp:contribution
|
attention mechanism for graph-structured data
handles graphs with varying neighborhood sizes
improved performance on node classification
node-level attention
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gptkbp:field
|
gptkb:machine_learning
graph neural networks
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gptkbp:handles
|
inductive learning
transductive learning
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|
https://www.w3.org/2000/01/rdf-schema#label
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Graph Attention Networks
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gptkbp:input
|
adjacency matrix
node features
|
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gptkbp:inspiredBy
|
attention mechanism
|
|
gptkbp:introduced
|
gptkb:Yoshua_Bengio
gptkb:Adriana_Romero
gptkb:Arantxa_Casanova
gptkb:Guillem_Cucurull
gptkb:Petar_Veličković
gptkb:Pietro_Liò
|
|
gptkbp:introducedIn
|
2017
|
|
gptkbp:lossFunction
|
cross-entropy
|
|
gptkbp:mainPaperTitle
|
gptkb:Graph_Attention_Networks
|
|
gptkbp:mainPaperURL
|
https://arxiv.org/abs/1710.10903
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gptkbp:openSource
|
gptkb:TensorFlow_GNN
gptkb:PyTorch_Geometric
DGL
|
|
gptkbp:output
|
node labels
node embeddings
|
|
gptkbp:platform
|
deep learning
|
|
gptkbp:publishedIn
|
gptkb:ICLR_2018
|
|
gptkbp:relatedTo
|
gptkb:GraphSAGE
gptkb:Graph_Convolutional_Networks
|
|
gptkbp:type
|
self-attention
|
|
gptkbp:usedIn
|
biological network analysis
recommendation systems
social network analysis
chemoinformatics
|
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gptkbp:bfsParent
|
gptkb:Nvidia_cuGraph
gptkb:Graph_Attention_Network
|
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gptkbp:bfsLayer
|
6
|