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SOTA
Node Classification
Node Classification On Ppi
Node Classification On Ppi
評価指標
F1
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
F1
Paper Title
Repository
SGAS
99.46
SGAS: Sequential Greedy Architecture Search
g2-MLP
99.71
A Proposal of Multi-Layer Perceptron with Graph Gating Unit for Graph Representation Learning and its Application to Surrogate Model for FEM
JK-LSTM
97.6
Representation Learning on Graphs with Jumping Knowledge Networks
LGCN
77.2
Large-Scale Learnable Graph Convolutional Networks
GCNII*
99.56
Simple and Deep Graph Convolutional Networks
DenseMRGCN-14
99.43
DeepGCNs: Making GCNs Go as Deep as CNNs
GCN + SAF
99.38 ± 0.01%
The Split Matters: Flat Minima Methods for Improving the Performance of GNNs
ClusterGCN
92.9
Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks
DSGCN
99.09 ± 0.03
Bridging the Gap Between Spectral and Spatial Domains in Graph Neural Networks
PairE
-
Graph Representation Learning Beyond Node and Homophily
GraphStar
99.4
Graph Star Net for Generalized Multi-Task Learning
ResMRGCN-28
99.41
DeepGCNs: Making GCNs Go as Deep as CNNs
GraphNAS
98.6 ± 0.1
GraphNAS: Graph Neural Architecture Search with Reinforcement Learning
GRACE
66.2
Deep Graph Contrastive Representation Learning
GaAN
98.7
GaAN: Gated Attention Networks for Learning on Large and Spatiotemporal Graphs
GraphSAINT
99.50
GraphSAINT: Graph Sampling Based Inductive Learning Method
GAT
97.3
Graph Attention Networks
SIGN
96.50
SIGN: Scalable Inception Graph Neural Networks
GAT + PGN
99.34 ± 0.02%
The Split Matters: Flat Minima Methods for Improving the Performance of GNNs
Cluster-GCN
99.36
Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks
0 of 22 row(s) selected.
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