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TASK | DATASET | MODEL | METRIC NAME | METRIC VALUE | GLOBAL RANK | REMOVE |
---|---|---|---|---|---|---|
Node Classification | Brazil Air-Traffic | GAT (Velickovic et al., 2018) | Accuracy | 0.382 | # 7 |
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Node Classification | Chameleon (60%/20%/20% random splits) | GAT | 1:1 Accuracy | 63.9 ± 0.46 | # 18 |
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Node Classification on Non-Homophilic (Heterophilic) Graphs | Chameleon(60%/20%/20% random splits) | GAT | 1:1 Accuracy | 63.9 ± 0.46 | # 18 |
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Graph Classification | CIFAR10 100k | GAT | Accuracy (%) | 65.48 | # 10 |
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Node Classification | Citeseer | GAT | Accuracy | 72.5 ± 0.7% | # 43 |
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Node Classification | Citeseer | GAT | Training Split | fixed 20 per node | # 3 |
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Node Classification | Citeseer | GAT | Validation | YES | # 1 |
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Node Classification | CiteSeer (0.5%) | GAT | Accuracy | 38.2% | # 13 |
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Node Classification | CiteSeer (1%) | GAT | Accuracy | 46.5% | # 14 |
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Node Classification | CiteSeer (60%/20%/20% random splits) | GAT | 1:1 Accuracy | 67.20 ± 0.46 | # 31 |
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Node Classification | CiteSeer with Public Split: fixed 20 nodes per class | GAT | Accuracy | 72.5 ± 0.7% | # 26 |
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Document Classification | Cora | GAT | Accuracy | 83.0% | # 3 |
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Node Classification | Cora | GAT | Accuracy | 83.0% ± 0.7% | # 44 |
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Node Classification | Cora | GAT | Training Split | fixed 20 per node | # 3 |
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Node Classification | Cora | GAT | Validation | YES | # 1 |
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Node Classification | Cora (0.5%) | GAT | Accuracy | 41.4% | # 13 |
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Node Classification | Cora (1%) | GAT | Accuracy | 48.6% | # 14 |
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Node Classification | Cora (3%) | GAT | Accuracy | 56.8% | # 15 |
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Node Classification | Cora (60%/20%/20% random splits) | GAT | 1:1 Accuracy | 76.70 ± 0.42 | # 30 |
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Node Classification | Cora with Public Split: fixed 20 nodes per class | GAT | Accuracy | 83.0 ± 0.7% | # 21 |
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Node Classification on Non-Homophilic (Heterophilic) Graphs | Cornell (60%/20%/20% random splits) | GAT | 1:1 Accuracy | 76.00 ± 1.01 | # 26 |
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Node Classification | Cornell (60%/20%/20% random splits) | GAT | 1:1 Accuracy | 76.00 ± 1.01 | # 26 |
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Node Classification on Non-Homophilic (Heterophilic) Graphs | Deezer-Europe | GAT | 1:1 Accuracy | 61.09±0.77 | # 22 |
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Node Classification | Europe Air-Traffic | GAT (Velickovic et al., 2018) | Accuracy | 42.4 | # 5 |
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Node Classification | Film (60%/20%/20% random splits) | GAT | 1:1 Accuracy | 35.98 ± 0.23 | # 26 |
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Node Classification | Flickr | GAT (Velickovic et al., 2018) | Accuracy | 0.359 | # 8 |
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Node Classification on Non-Homophilic (Heterophilic) Graphs | genius | GAT | 1:1 Accuracy | 55.80 ± 0.87 | # 28 |
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Node Classification | genius | GAT | Accuracy | 55.80 ± 0.87 | # 25 |
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Skeleton Based Action Recognition | J-HMBD Early Action | GAT | 10% | 58.1 | # 2 |
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Graph Regression | Lipophilicity | GAT | RMSE | 0.95 | # 9 |
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Graph Property Prediction | ogbg-code2 | GAT | Test F1 score | 0.1569 ± 0.0010 | # 15 |
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Graph Property Prediction | ogbg-code2 | GAT | Validation F1 score | 0.1442 ± 0.0017 | # 14 |
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Graph Property Prediction | ogbg-code2 | GAT | Number of params | 11030210 | # 13 |
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Graph Property Prediction | ogbg-code2 | GAT | Ext. data | No | # 1 |
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Node Property Prediction | ogbn-arxiv | GAT+label reuse+self KD | Test Accuracy | 0.7416 ± 0.0008 | # 23 |
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Node Property Prediction | ogbn-arxiv | GAT+label reuse+self KD | Validation Accuracy | 0.7514 ± 0.0004 | # 28 |
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Node Property Prediction | ogbn-arxiv | GAT+label reuse+self KD | Number of params | 1441580 | # 31 |
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Node Property Prediction | ogbn-arxiv | GAT+label reuse+self KD | Ext. data | No | # 1 |
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Node Property Prediction | ogbn-arxiv | GAT+label+reuse+topo loss | Test Accuracy | 0.7399 ± 0.0012 | # 29 |
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Node Property Prediction | ogbn-arxiv | GAT+label+reuse+topo loss | Validation Accuracy | 0.7513 ± 0.0009 | # 29 |
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Node Property Prediction | ogbn-arxiv | GAT+label+reuse+topo loss | Number of params | 1441580 | # 31 |
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Node Property Prediction | ogbn-arxiv | GAT+label+reuse+topo loss | Ext. data | No | # 1 |
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Node Property Prediction | ogbn-products | GAT with NeighborSampling | Test Accuracy | 0.7945 ± 0.0059 | # 45 |
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Node Property Prediction | ogbn-products | GAT with NeighborSampling | Validation Accuracy | Please tell us | # 57 |
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Node Property Prediction | ogbn-products | GAT with NeighborSampling | Number of params | 751574 | # 33 |
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Node Property Prediction | ogbn-products | GAT with NeighborSampling | Ext. data | No | # 1 |
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Node Property Prediction | ogbn-proteins | GAT + labels + node2vec | Test ROC-AUC | 0.8711 ± 0.0007 | # 8 |
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Node Property Prediction | ogbn-proteins | GAT + labels + node2vec | Validation ROC-AUC | 0.9217 ± 0.0011 | # 9 |
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Node Property Prediction | ogbn-proteins | GAT + labels + node2vec | Number of params | 6360470 | # 8 |
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Node Property Prediction | ogbn-proteins | GAT + labels + node2vec | Ext. data | No | # 1 |
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Node Classification | PATTERN 100k | GAT | Accuracy (%) | 75.824 | # 8 |
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Node Classification | Penn94 | GAT | Accuracy | 81.53 ± 0.55 | # 14 |
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Node Classification on Non-Homophilic (Heterophilic) Graphs | Penn94 | GAT | 1:1 Accuracy | 81.53 ± 0.55 | # 14 |
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Node Classification | PPI | GAT | F1 | 97.3 | # 16 |
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Node Classification | Pubmed | GAT | Accuracy | 79.0 | # 52 |
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Node Classification | Pubmed | GAT | Training Split | fixed 20 per node | # 4 |
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Node Classification | Pubmed | GAT | Validation | YES | # 1 |
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Node Classification | PubMed (0.03%) | GAT | Accuracy | 50.9% | # 12 |
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Node Classification | PubMed (0.05%) | GAT | Accuracy | 50.4% | # 13 |
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Node Classification | PubMed (0.1%) | GAT | Accuracy | 59.6% | # 13 |
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Node Classification | PubMed (60%/20%/20% random splits) | GAT | 1:1 Accuracy | 83.28 ± 0.12 | # 34 |
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Node Classification | PubMed with Public Split: fixed 20 nodes per class | GAT | Accuracy | 79.0% | # 22 |
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Node Classification | Squirrel (60%/20%/20% random splits) | GAT | 1:1 Accuracy | 42.72 ± 0.33 | # 22 |
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Node Classification | Texas (60%/20%/20% random splits) | GAT | 1:1 Accuracy | 78.87 ± 0.86 | # 31 |
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Node Classification on Non-Homophilic (Heterophilic) Graphs | Texas(60%/20%/20% random splits) | GAT | 1:1 Accuracy | 78.87 ± 0.86 | # 29 |
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