| Universal Graph Transformer Self-Attention Networks | ✓ Link | 89.2% | | U2GNN (Unsupervised) | 2019-09-26 |
| Template based Graph Neural Network with Optimal Transport Distances | ✓ Link | 56.8% | | TFGW ADJ (L=2) | 2022-05-31 |
| TREE-G: Decision Trees Contesting Graph Neural Networks | ✓ Link | 56.4% | | TREE-G | 2022-07-06 |
| Maximum Entropy Weighted Independent Set Pooling for Graph Neural Networks | ✓ Link | 56.23% | | MEWISPool | 2021-07-03 |
| Learning Universal Graph Neural Network Embeddings With Aid Of Transfer Learning | ✓ Link | 56.10% | | DUGNN | 2019-09-22 |
| When Work Matters: Transforming Classical Network Structures to Graph CNN | | 54.53% | | G_ResNet | 2018-07-07 |
| Mutual Information Maximization in Graph Neural Networks | ✓ Link | 54.52% | | sGIN | 2019-05-21 |
| Graph isomorphism UNet | ✓ Link | 54% | | GIUNet | 2023-08-23 |
| Universal Graph Transformer Self-Attention Networks | ✓ Link | 53.60% | | U2GNN | 2019-09-26 |
| Segmented Graph-Bert for Graph Instance Modeling | ✓ Link | 53.4% | | SEG-BERT | 2020-02-09 |
| How Powerful are Graph Neural Networks? | ✓ Link | 52.3% | | GIN-0 | 2018-10-01 |
| Wasserstein Embedding for Graph Learning | ✓ Link | 52% | | WEGL | 2020-06-16 |
| Are Powerful Graph Neural Nets Necessary? A Dissection on Graph Classification | ✓ Link | 51.80% | | GFN | 2019-05-11 |
| Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks | ✓ Link | 51.5% | | 1-WL Kernel | 2018-10-04 |
| DropGNN: Random Dropouts Increase the Expressiveness of Graph Neural Networks | ✓ Link | 51.4% | | DropGIN | 2021-11-11 |
| Are Powerful Graph Neural Nets Necessary? A Dissection on Graph Classification | ✓ Link | 51.20% | | GFN-light | 2019-05-11 |
| Unsupervised Inductive Graph-Level Representation Learning via Graph-Graph Proximity | ✓ Link | 50.97% | | UGraphEmb-F | 2019-04-01 |
| Graph-level Representation Learning with Joint-Embedding Predictive Architectures | ✓ Link | 50.69% | | Graph-JEPA | 2023-09-27 |
| Accurate Learning of Graph Representations with Graph Multiset Pooling | ✓ Link | 50.66% | | GMT | 2021-02-23 |
| Online Graph Dictionary Learning | ✓ Link | 50.64% | | GDL | 2021-02-12 |
| Weisfeiler and Leman go sparse: Towards scalable higher-order graph embeddings | ✓ Link | 50.5% | | δ-2-LWL | 2019-04-02 |
| Capsule Graph Neural Network | ✓ Link | 50.27% | | CapsGNN | 2019-05-01 |
| Unsupervised Inductive Graph-Level Representation Learning via Graph-Graph Proximity | ✓ Link | 50.06% | | UGraphEmb | 2019-04-01 |
| Strengthening structural baselines for graph classification using Local Topological Profile | ✓ Link | 50.0 ± 4.6 | 50.0 ± 4.6 | Local Topological Profile (LTP) | 2023-05-01 |
| Provably Powerful Graph Networks | ✓ Link | 50% | | PPGN | 2019-05-27 |
| InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization | ✓ Link | 49.69% | | InfoGraph | 2019-07-31 |
| Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks | ✓ Link | 49.5% | | k-GNN | 2018-10-04 |
| Learning metrics for persistence-based summaries and applications for graph classification | ✓ Link | 49.5% | | WKPI-kcenters | 2019-04-27 |
| Graph Representation Learning via Hard and Channel-Wise Attention Networks | ✓ Link | 49.06% | | hGANet | 2019-07-05 |
| Rep the Set: Neural Networks for Learning Set Representations | ✓ Link | 48.92% | | ApproxRepSet | 2019-04-03 |
| An End-to-End Deep Learning Architecture for Graph Classification | ✓ Link | 47.83% | | DGCNN | 2018-04-29 |
| A Fair Comparison of Graph Neural Networks for Graph Classification | ✓ Link | 47.6% | | GraphSAGE | 2019-12-20 |
| Deep Graph Kernels | | 44.55% | | DGK | 2015-08-10 |
| SPI-GCN: A Simple Permutation-Invariant Graph Convolutional Network | | 44.13% | | SPI-GCN | 2019-04-08 |
| An End-to-End Deep Learning Architecture for Graph Classification | ✓ Link | 42.76% | | DGCNN (sum) | 2018-04-29 |
| Fine-tuning Graph Neural Networks by Preserving Graph Generative Patterns | ✓ Link | | 51.80 | G-Tuning | 2023-12-21 |