| A Proposal of Multi-Layer Perceptron with Graph Gating Unit for Graph Representation Learning and its Application to Surrogate Model for FEM | ✓ Link | 99.71 | | | | g2-MLP | 2022-07-11 |
| Simple and Deep Graph Convolutional Networks | ✓ Link | 99.56 | | | | GCNII* | 2020-07-04 |
| GraphSAINT: Graph Sampling Based Inductive Learning Method | ✓ Link | 99.50 | | | | GraphSAINT | 2019-07-10 |
| SGAS: Sequential Greedy Architecture Search | ✓ Link | 99.46 | | | | SGAS | 2019-11-30 |
| DeepGCNs: Making GCNs Go as Deep as CNNs | ✓ Link | 99.43 | | | | DenseMRGCN-14 | 2019-10-15 |
| DeepGCNs: Making GCNs Go as Deep as CNNs | ✓ Link | 99.41 | | | | ResMRGCN-28 | 2019-10-15 |
| Graph Star Net for Generalized Multi-Task Learning | ✓ Link | 99.4 | | | | GraphStar | 2019-06-21 |
| The Split Matters: Flat Minima Methods for Improving the Performance of GNNs | ✓ Link | 99.38 ± 0.01% | | | | GCN + SAF | 2023-06-15 |
| Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks | ✓ Link | 99.36 | | | | Cluster-GCN | 2019-05-20 |
| The Split Matters: Flat Minima Methods for Improving the Performance of GNNs | ✓ Link | 99.34 ± 0.02% | | | | GAT + PGN | 2023-06-15 |
| Bridging the Gap Between Spectral and Spatial Domains in Graph Neural Networks | ✓ Link | 99.09 ± 0.03 | | | | DSGCN | 2020-03-26 |
| GaAN: Gated Attention Networks for Learning on Large and Spatiotemporal Graphs | ✓ Link | 98.7 | | | | GaAN | 2018-03-20 |
| GraphNAS: Graph Neural Architecture Search with Reinforcement Learning | ✓ Link | 98.6 ± 0.1 | | | | GraphNAS | 2019-04-22 |
| Representation Learning on Graphs with Jumping Knowledge Networks | ✓ Link | 97.6 | | | | JK-LSTM | 2018-06-09 |
| VQ-GNN: A Universal Framework to Scale up Graph Neural Networks using Vector Quantization | ✓ Link | 97.37 | | | | VQ-GNN (GAT) | 2021-10-27 |
| Graph Attention Networks | ✓ Link | 97.3 | | | | GAT | 2017-10-30 |
| SIGN: Scalable Inception Graph Neural Networks | ✓ Link | 96.50 | | | | SIGN | 2020-04-23 |
| Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks | ✓ Link | 92.9 | | | | ClusterGCN | 2019-05-20 |
| Large-Scale Learnable Graph Convolutional Networks | ✓ Link | 77.2 | | | | LGCN | 2018-08-12 |
| Deep Graph Contrastive Representation Learning | ✓ Link | 66.2 | 66.2 | | | GRACE | 2020-06-07 |
| Inductive Representation Learning on Large Graphs | ✓ Link | 61.2 | | | | GraphSAGE | 2017-06-07 |
| Graph Representation Learning Beyond Node and Homophily | ✓ Link | | | 94.83 | | PairE | 2022-03-03 |
| node2vec: Scalable Feature Learning for Networks | ✓ Link | | | | 0.1791 | node2vec | 2016-07-03 |
| node2vec: Scalable Feature Learning for Networks | ✓ Link | | | | 0.1768 | DeepWalk | 2016-07-03 |