| GNNDLD: Graph Neural Network with Directional Label Distribution | | 77.72±0.84 | GNNDLD | 2024-02-26 |
| Revisiting Heterophily For Graph Neural Networks | ✓ Link | 69.98 ± 1.53 | ACMII-GCN++ | 2022-10-14 |
| Revisiting Heterophily For Graph Neural Networks | ✓ Link | 69.81 ± 1.11 | ACMII-GCN+ | 2022-10-14 |
| Revisiting Heterophily For Graph Neural Networks | ✓ Link | 69.26 ± 1.11 | ACM-GCN+ | 2022-10-14 |
| Revisiting Heterophily For Graph Neural Networks | ✓ Link | 68.56 ± 1.33 | ACM-GCN++ | 2022-10-14 |
| Node-oriented Spectral Filtering for Graph Neural Networks | ✓ Link | 58.9±0.35 | NFGNN | 2022-12-07 |
| Revisiting Heterophily For Graph Neural Networks | ✓ Link | 55.97 ± 2.03 | ACM-Snowball-2 | 2022-10-14 |
| Revisiting Heterophily For Graph Neural Networks | ✓ Link | 55.73 ± 2.39 | ACM-Snowball-3 | 2022-10-14 |
| Revisiting Heterophily For Graph Neural Networks | ✓ Link | 54.53 ± 2.09 | ACMII-GCN | 2022-10-14 |
| Revisiting Heterophily For Graph Neural Networks | ✓ Link | 53.48 ± 0.6 | ACMII-Snowball-2 | 2022-10-14 |
| Revisiting Heterophily For Graph Neural Networks | ✓ Link | 53.40 ± 1.90 | GCN+JK | 2022-10-14 |
| Revisiting Heterophily For Graph Neural Networks | ✓ Link | 52.31 ± 1.57 | ACMII-Snowball-3 | 2022-10-14 |
| Revisiting Heterophily For Graph Neural Networks | ✓ Link | 52.28 ± 3.61 | GAT+JK | 2022-10-14 |
| BernNet: Learning Arbitrary Graph Spectral Filters via Bernstein Approximation | ✓ Link | 51.35 ± 0.73 | BernNet | 2021-06-21 |
| Adaptive Universal Generalized PageRank Graph Neural Network | ✓ Link | 49.93 ± 0.53 | GPRGNN | 2020-06-14 |
| Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks | ✓ Link | 48.25 ± 0.94 | Snowball-3 | 2019-06-05 |
| Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks | ✓ Link | 47.88 ± 1.23 | Snowball-2 | 2019-06-05 |
| Simplifying Graph Convolutional Networks | ✓ Link | 47.62 ± 1.27 | SGC-1 | 2019-02-19 |
| Half-Hop: A graph upsampling approach for slowing down message passing | ✓ Link | 47.19 ± 1.21 | HH-GCN | 2023-08-17 |
| Revisiting Heterophily For Graph Neural Networks | ✓ Link | 46.4 ± 1.13 | ACM-SGC-1 | 2022-10-14 |
| Half-Hop: A graph upsampling approach for slowing down message passing | ✓ Link | 46.35 ± 1.86 | HH-GAT | 2023-08-17 |
| Half-Hop: A graph upsampling approach for slowing down message passing | ✓ Link | 45.25 ± 1.52 | HH-GraphSAGE | 2023-08-17 |
| Semi-Supervised Classification with Graph Convolutional Networks | ✓ Link | 44.76 ± 1.39 | GCN | 2016-09-09 |
| Graph Attention Networks | ✓ Link | 42.72 ± 0.33 | GAT | 2017-10-30 |
| Beyond Low-frequency Information in Graph Convolutional Networks | ✓ Link | 42.24 ± 1.2 | FAGCN | 2021-01-04 |
| Inductive Representation Learning on Large Graphs | ✓ Link | 41.26 ± 0.26 | GraphSAGE | 2017-06-07 |
| Simplifying Graph Convolutional Networks | ✓ Link | 41.25 ± 1.4 | SGC-2 | 2019-02-19 |
| Revisiting Heterophily For Graph Neural Networks | ✓ Link | 40.91 ± 1.39 | ACM-SGC-2 | 2022-10-14 |
| Revisiting Heterophily For Graph Neural Networks | ✓ Link | 40.9 ± 1.58 | ACM-GCNII | 2022-10-14 |
| Simple and Deep Graph Convolutional Networks | ✓ Link | 38.81 ± 1.97 | GCNII | 2020-07-04 |
| Revisiting Heterophily For Graph Neural Networks | ✓ Link | 38.32 ± 1.5 | ACM-GCNII* | 2022-10-14 |
| Simple and Deep Graph Convolutional Networks | ✓ Link | 38.31 ± 1.3 | GCNII* | 2020-07-04 |
| Geom-GCN: Geometric Graph Convolutional Networks | ✓ Link | 38.14 | Geom-GCN* | 2020-02-13 |
| Predict then Propagate: Graph Neural Networks meet Personalized PageRank | ✓ Link | 34.77 ± 0.34 | APPNP | 2018-10-14 |
| Revisiting Heterophily For Graph Neural Networks | ✓ Link | 31.28 ± 0.27 | MLP-2 | 2022-10-14 |
| Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Designs | ✓ Link | 30.39 ± 1.22 | H2GCN | 2020-06-20 |
| MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing | ✓ Link | 24.55 ± 2.60 | MixHop | 2019-04-30 |