| Universal Graph Transformer Self-Attention Networks | ✓ Link | 91.81% | U2GNN (Unsupervised) | 2019-09-26 |
| Graph isomorphism UNet | ✓ Link | 85.7% | GIUNet | 2023-08-23 |
| Gaussian-Induced Convolution for Graphs | | 77.64% | GIC | 2018-11-11 |
| Learning Universal Graph Neural Network Embeddings With Aid Of Transfer Learning | ✓ Link | 74.7% | DUGNN | 2019-09-22 |
| Unsupervised Inductive Graph-Level Representation Learning via Graph-Graph Proximity | ✓ Link | 73.56% | UGraphEmb-F | 2019-04-01 |
| Mutual Information Maximization in Graph Neural Networks | ✓ Link | 73.56% | sGIN | 2019-05-21 |
| When Work Matters: Transforming Classical Network Structures to Graph CNN | | 73.24% | G_DenseNet | 2018-07-07 |
| CIN++: Enhancing Topological Message Passing | ✓ Link | 73.2% | CIN++ | 2023-06-06 |
| Cell Attention Networks | ✓ Link | 72.8% | CAN | 2022-09-16 |
| Unsupervised Inductive Graph-Level Representation Learning via Graph-Graph Proximity | ✓ Link | 72.54% | UGraphEmb | 2019-04-01 |
| Template based Graph Neural Network with Optimal Transport Distances | ✓ Link | 72.4% | TFGW ADJ (L=2) | 2022-05-31 |
| Discriminative Graph Autoencoder | | 71.24% | DGA | 2018-11-17 |
| Universal Graph Transformer Self-Attention Networks | ✓ Link | 69.63% | U2GNN | 2019-09-26 |
| Capsule Neural Networks for Graph Classification using Explicit Tensorial Graph Representations | | 69% | BC + Capsules | 2019-02-22 |
| Segmented Graph-Bert for Graph Instance Modeling | ✓ Link | 68.86% | SEG-BERT | 2020-02-09 |
| A New Perspective on the Effects of Spectrum in Graph Neural Networks | ✓ Link | 68.05% | Spec-GN | 2021-12-14 |
| Wasserstein Embedding for Graph Learning | ✓ Link | 67.5% | WEGL | 2020-06-16 |
| Wasserstein Weisfeiler-Lehman Graph Kernels | ✓ Link | 66.31% | WWL | 2019-06-04 |
| DropGNN: Random Dropouts Increase the Expressiveness of Graph Neural Networks | ✓ Link | 66.3% | DropGIN | 2021-11-11 |
| Provably Powerful Graph Networks | ✓ Link | 66.17% | PPGN | 2019-05-27 |
| Subgraph Networks with Application to Structural Feature Space Expansion | | 65.88% | Deep WL SGN(0,1,2) | 2019-03-21 |
| DGCNN: Disordered Graph Convolutional Neural Network Based on the Gaussian Mixture Model | | 65.43% | DGCNN | 2017-12-10 |
| Graph Representation Learning via Hard and Channel-Wise Attention Networks | ✓ Link | 65.02% | hGANet | 2019-07-05 |
| How Powerful are Graph Neural Networks? | ✓ Link | 64.40% | GIN-0 | 2018-10-01 |
| Graph Representation Learning via Hard and Channel-Wise Attention Networks | ✓ Link | 63.53% | cGANet | 2019-07-05 |
| DDGK: Learning Graph Representations for Deep Divergence Graph Kernels | ✓ Link | 63.14% | DDGK | 2019-04-21 |
| Graph Representation Learning via Hard and Channel-Wise Attention Networks | ✓ Link | 62.94% | GANet | 2019-07-05 |
| DAGCN: Dual Attention Graph Convolutional Networks | ✓ Link | 62.88% | DAGCN | 2019-04-04 |
| A Simple Baseline Algorithm for Graph Classification | ✓ Link | 62.8% | SF + RFC | 2018-10-22 |
| Weisfeiler and Leman go sparse: Towards scalable higher-order graph embeddings | ✓ Link | 62.70% | δ-2-LWL | 2019-04-02 |
| A simple yet effective baseline for non-attributed graph classification | ✓ Link | 61.7% | LDP | 2018-11-08 |
| InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization | ✓ Link | 61.65 | InfoGraph | 2019-07-31 |
| graph2vec: Learning Distributed Representations of Graphs | ✓ Link | 60.17% ± 6.86% | graph2vec | 2017-07-17 |
| Learning Convolutional Neural Networks for Graphs | ✓ Link | 60.00% | PATCHY-SAN | 2016-05-17 |
| IsoNN: Isomorphic Neural Network for Graph Representation Learning and Classification | ✓ Link | 59.9% | IsoNN | 2019-07-22 |
| TREE-G: Decision Trees Contesting Graph Neural Networks | ✓ Link | 59.1% | TREE-G | 2022-07-06 |
| SPI-GCN: A Simple Permutation-Invariant Graph Convolutional Network | | 56.41% | SPI-GCN | 2019-04-08 |