| Simple and Deep Graph Convolutional Networks | ✓ Link | 90.15 ± 0.43 | | GCNII | 2020-07-04 |
| Geom-GCN: Geometric Graph Convolutional Networks | ✓ Link | 89.95 ± 0.47 | | Geom-GCN | 2020-02-13 |
| Revisiting Heterophily For Graph Neural Networks | ✓ Link | 89.89 ± 0.43 | | ACMII-GCN | 2022-10-14 |
| Revisiting Heterophily For Graph Neural Networks | ✓ Link | 89.82 ± 0.41 | | ACM-GCN+ | 2022-10-14 |
| Revisiting Heterophily For Graph Neural Networks | ✓ Link | 89.78 ± 0.49 | | ACMII-GCN+ | 2022-10-14 |
| Revisiting Heterophily For Graph Neural Networks | ✓ Link | 89.71 ± 0.48 | | ACMII-GCN++ | 2022-10-14 |
| Revisiting Heterophily For Graph Neural Networks | ✓ Link | 89.65 ± 0.58 | | ACM-GCN++ | 2022-10-14 |
| Finding Global Homophily in Graph Neural Networks When Meeting Heterophily | ✓ Link | 89.62 ± 0.35 | | GloGNN | 2022-05-15 |
| Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Designs | ✓ Link | 89.49 ± 0.38 | | H2GCN | 2020-06-20 |
| Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs | ✓ Link | 89.49 ± 0.40 | | O(d)-NSD | 2022-02-09 |
| Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs | ✓ Link | 89.42 ± 0.43 | | Diag-NSD | 2022-02-09 |
| Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs | ✓ Link | 89.33 ± 0.35 | | Gen-NSD | 2022-02-09 |
| Finding Global Homophily in Graph Neural Networks When Meeting Heterophily | ✓ Link | 89.24 ± 0.39 | | GloGNN++ | 2022-05-15 |
| Addressing Heterophily in Node Classification with Graph Echo State Networks | ✓ Link | 89.20 ± 0.34 | | GESN | 2023-05-14 |
| Two Sides of the Same Coin: Heterophily and Oversmoothing in Graph Convolutional Neural Networks | ✓ Link | 89.15 ± 0.37 | | GGCN | 2021-02-12 |
| Revisiting Heterophily For Graph Neural Networks | ✓ Link | 89.01 ± 0.6 | | ACM-SGC-2 | 2022-10-14 |
| Non-Local Graph Neural Networks | ✓ Link | 89.0 ± 0.5 | | NLGCN | 2020-05-29 |
| Breaking the Limit of Graph Neural Networks by Improving the Assortativity of Graphs with Local Mixing Patterns | ✓ Link | 88.52 ± 0.92 | | WRGAT | 2021-06-11 |
| Revisiting Heterophily For Graph Neural Networks | ✓ Link | 88.49 ± 0.51 | | ACM-SGC-1 | 2022-10-14 |
| Non-Local Graph Neural Networks | ✓ Link | 88.2 ± 0.5 | | NLMLP | 2020-05-29 |
| Non-Local Graph Neural Networks | ✓ Link | 88.2 ± 0.3 | | NLGAT | 2020-05-29 |
| Beyond Low-frequency Information in Graph Convolutional Networks | ✓ Link | 88.09 ± 1.38 | | FAGCN | 2021-01-04 |
| Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods | ✓ Link | 87.86 ± 0.77 | | LINKX | 2021-10-27 |
| Adaptive Universal Generalized PageRank Graph Neural Network | ✓ Link | 87.54 ± 0.38 | | GPRGCN | 2020-06-14 |
| MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing | ✓ Link | 85.31 ± 0.61 | | MixHop | 2019-04-30 |
| GREAD: Graph Neural Reaction-Diffusion Networks | ✓ Link | | 90.21 | GREAD-BS | 2022-11-25 |