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SOTA
图属性预测
Graph Property Prediction On Ogbg Ppa
Graph Property Prediction On Ogbg Ppa
评估指标
Ext. data
Number of params
Test Accuracy
Validation Accuracy
评测结果
各个模型在此基准测试上的表现结果
Columns
模型名称
Ext. data
Number of params
Test Accuracy
Validation Accuracy
Paper Title
Repository
ExpC
No
1369397
0.7976 ± 0.0072
0.7518 ± 0.0080
Breaking the Expressive Bottlenecks of Graph Neural Networks
GCN
No
479437
0.6839 ± 0.0084
0.6497 ± 0.0034
Semi-Supervised Classification with Graph Convolutional Networks
GatedGCN+
No
5547557
0.8258 ± 0.0055
0.7815 ± 0.0043
Can Classic GNNs Be Strong Baselines for Graph-level Tasks? Simple Architectures Meet Excellence
GCN+
No
5549605
0.8077 ± 0.0041
0.7586 ± 0.0032
Can Classic GNNs Be Strong Baselines for Graph-level Tasks? Simple Architectures Meet Excellence
GC-T+MCL(6.0)
No
4006704
0.7432 ± 0.0033
0.6989 ± 0.0037
-
-
DeeperGCN
No
2336421
0.7712 ± 0.0071
0.7313 ± 0.0078
DeeperGCN: All You Need to Train Deeper GCNs
GIN+virtual node
No
3288042
0.7037 ± 0.0107
0.6678 ± 0.0105
How Powerful are Graph Neural Networks?
DeeperGCN+FLAG
No
2336421
0.7752 ± 0.0069
0.7484 ± 0.0052
Robust Optimization as Data Augmentation for Large-scale Graphs
GIN+FLAG
No
1836942
0.6905 ± 0.0092
0.6465 ± 0.0070
Robust Optimization as Data Augmentation for Large-scale Graphs
PAS+F2GNN
No
16346166
0.8201 ± 0.0019
0.7720 ± 0.0023
-
-
GIN+
No
8173605
0.8107 ± 0.0053
0.7786 ± 0.0095
Can Classic GNNs Be Strong Baselines for Graph-level Tasks? Simple Architectures Meet Excellence
PAS
No
3717160
0.7828 ± 0.0024
0.7523 ± 0.0028
-
-
GCN+virtual node+FLAG
No
1930537
0.6944 ± 0.0052
0.6638 ± 0.0055
Robust Optimization as Data Augmentation for Large-scale Graphs
GIN+virtual node+FLAG
No
3288042
0.7245 ± 0.0114
0.6789 ± 0.0079
Robust Optimization as Data Augmentation for Large-scale Graphs
GIN
No
1836942
0.6892 ± 0.0100
0.6562 ± 0.0107
How Powerful are Graph Neural Networks?
ExpC*+bag of tricks
No
3758642
0.8140 ± 0.0028
0.7811 ± 0.0012
-
-
GCN+virtual node
No
1930537
0.6857 ± 0.0061
0.6511 ± 0.0048
Semi-Supervised Classification with Graph Convolutional Networks
GPS
No
3434533
0.8015
0.7556 ± 0.0027
Recipe for a General, Powerful, Scalable Graph Transformer
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