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SOTA
グラフ分類
Graph Classification On Cifar10 100K
Graph Classification On Cifar10 100K
評価指標
Accuracy (%)
評価結果
このベンチマークにおける各モデルのパフォーマンス結果
Columns
モデル名
Accuracy (%)
Paper Title
NeuralWalker
80.027 ± 0.185
Learning Long Range Dependencies on Graphs via Random Walks
GatedGCN+
77.218 ± 0.381
Can Classic GNNs Be Strong Baselines for Graph-level Tasks? Simple Architectures Meet Excellence
GRED
76.853±0.185
Recurrent Distance Filtering for Graph Representation Learning
GRIT
76.468
Graph Inductive Biases in Transformers without Message Passing
GraphGPS + HDSE
76.180±0.277
Enhancing Graph Transformers with Hierarchical Distance Structural Encoding
ESA (Edge set attention, no positional encodings)
75.413±0.248
An end-to-end attention-based approach for learning on graphs
Exphormer
74.754±0.194
Exphormer: Sparse Transformers for Graphs
TIGT
73.955
Topology-Informed Graph Transformer
ARGNP
73.90
Automatic Relation-aware Graph Network Proliferation
DGN
72.84
Directional Graph Networks
GPS
72.298
Recipe for a General, Powerful, Scalable Graph Transformer
PNA
70.47
Principal Neighbourhood Aggregation for Graph Nets
EIGENFORMER
70.194
Graph Transformers without Positional Encodings
GatedGCN
69.37
Residual Gated Graph ConvNets
EGT
68.702
Global Self-Attention as a Replacement for Graph Convolution
GatedGCN
67.312
Benchmarking Graph Neural Networks
GraphSage
66.08
Inductive Representation Learning on Large Graphs
GAT
65.48
Graph Attention Networks
MoNet
53.42
Geometric deep learning on graphs and manifolds using mixture model CNNs
GIN
53.28
How Powerful are Graph Neural Networks?
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Graph Classification On Cifar10 100K | SOTA | HyperAI超神経