| Scale Invariance of Graph Neural Networks | ✓ Link | 76.0±2.0 | ScaleNet | 2024-11-28 |
| Edge Directionality Improves Learning on Heterophilic Graphs | ✓ Link | 75.31±1.92 | Dir-GNN | 2023-05-17 |
| Addressing Heterophily in Node Classification with Graph Echo State Networks | ✓ Link | 73.56 ± 1.62 | GESN | 2023-05-14 |
| Revisiting Heterophily For Graph Neural Networks | ✓ Link | 67.4 ± 2.21 | ACMII-GCN++ | 2022-10-14 |
| Revisiting Heterophily For Graph Neural Networks | ✓ Link | 67.07 ± 1.65 | ACMII-GCN+ | 2022-10-14 |
| Revisiting Heterophily For Graph Neural Networks | ✓ Link | 67.06 ± 1.66 | ACM-GCN++ | 2022-10-14 |
| Revisiting Heterophily For Graph Neural Networks | ✓ Link | 66.98 ± 1.71 | ACM-GCN+ | 2022-10-14 |
| Deformable Graph Convolutional Networks | ✓ Link | 62.56 ± 1.31 | Deformable GCN | 2021-12-29 |
| Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods | ✓ Link | 61.81 ± 1.80 | LINKX | 2021-10-27 |
| Non-Local Graph Neural Networks | ✓ Link | 59.0 ± 1.2 | NLGCN | 2020-05-29 |
| Finding Global Homophily in Graph Neural Networks When Meeting Heterophily | ✓ Link | 57.88 ± 1.76 | GloGNN++ | 2022-05-15 |
| Finding Global Homophily in Graph Neural Networks When Meeting Heterophily | ✓ Link | 57.54 ± 1.39 | GloGNN | 2022-05-15 |
| Non-Local Graph Neural Networks | ✓ Link | 56.8 ± 2.5 | NLGAT | 2020-05-29 |
| Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs | ✓ Link | 56.34 ± 1.32 | O(d)-NSD | 2022-02-09 |
| Revisiting Heterophily For Graph Neural Networks | ✓ Link | 55.19 ± 1.49 | ACM-GCN | 2022-10-14 |
| Two Sides of the Same Coin: Heterophily and Oversmoothing in Graph Convolutional Neural Networks | ✓ Link | 55.17 ± 1.58 | GGCN | 2021-02-12 |
| Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs | ✓ Link | 54.78 ± 1.81 | Diag-NSD | 2022-02-09 |
| Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs | ✓ Link | 53.17 ± 1.31 | Gen-NSD | 2022-02-09 |
| Revisiting Heterophily For Graph Neural Networks | ✓ Link | 51.8 ± 1.5 | ACMII-GCN | 2022-10-14 |
| Breaking the Limit of Graph Neural Networks by Improving the Assortativity of Graphs with Local Mixing Patterns | ✓ Link | 48.85 ± 0.78 | WRGAT | 2021-06-11 |
| Adaptive Universal Generalized PageRank Graph Neural Network | ✓ Link | 46.31 ± 2.46 | GPRGCN | 2020-06-14 |
| Revisiting Heterophily For Graph Neural Networks | ✓ Link | 45.00 ± 1.4 | ACM-SGC-1 | 2022-10-14 |
| MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing | ✓ Link | 43.80 ± 1.48 | MixHop | 2019-04-30 |
| Revisiting Heterophily For Graph Neural Networks | ✓ Link | 40.02 ± 0.96 | ACM-SGC-2 | 2022-10-14 |
| Simple and Deep Graph Convolutional Networks | ✓ Link | 38.47 ± 1.58 | GCNII | 2020-07-04 |
| Geom-GCN: Geometric Graph Convolutional Networks | ✓ Link | 38.15 ± 0.92 | Geom-GCN | 2020-02-13 |
| Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Designs | ✓ Link | 36.48 ± 1.86 | H2GCN | 2020-06-20 |
| Non-Local Graph Neural Networks | ✓ Link | 33.7 ± 1.5 | NLMLP | 2020-05-29 |
| Beyond Low-frequency Information in Graph Convolutional Networks | ✓ Link | 30.83 ± 0.69 | FAGCN | 2021-01-04 |