| Revisiting Heterophily For Graph Neural Networks | ✓ Link | 76.08 ± 2.13 | ACM-GCN+ | 2022-10-14 |
| Revisiting Heterophily For Graph Neural Networks | ✓ Link | 75.93 ± 1.71 | ACMII-GCN++ | 2022-10-14 |
| Revisiting Heterophily For Graph Neural Networks | ✓ Link | 75.51 ± 1.58 | ACMII-GCN+ | 2022-10-14 |
| Revisiting Heterophily For Graph Neural Networks | ✓ Link | 75.23 ± 1.72 | ACM-GCN++ | 2022-10-14 |
| Revisiting Heterophily For Graph Neural Networks | ✓ Link | 68.51 ± 1.7 | ACM-Snowball-2 | 2022-10-14 |
| Revisiting Heterophily For Graph Neural Networks | ✓ Link | 68.4 ± 2.05 | ACM-Snowball-3 | 2022-10-14 |
| Revisiting Heterophily For Graph Neural Networks | ✓ Link | 68.38 ± 1.36 | ACMII-GCN | 2022-10-14 |
| BernNet: Learning Arbitrary Graph Spectral Filters via Bernstein Approximation | ✓ Link | 68.29 ± 1.58 | BernNet | 2021-06-21 |
| Revisiting Heterophily For Graph Neural Networks | ✓ Link | 68.14 ± 1.18 | GAT+JK | 2022-10-14 |
| Revisiting Heterophily For Graph Neural Networks | ✓ Link | 67.83 ± 2.63 | ACMII-Snowball-2 | 2022-10-14 |
| Revisiting Heterophily For Graph Neural Networks | ✓ Link | 67.53 ± 2.83 | ACMII-Snowball-3 | 2022-10-14 |
| Adaptive Universal Generalized PageRank Graph Neural Network | ✓ Link | 67.48 ± 0.40 | GPRGNN | 2020-06-14 |
| Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks | ✓ Link | 65.49 ± 1.64 | Snowball-3 | 2019-06-05 |
| Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks | ✓ Link | 64.99 ± 2.39 | Snowball-2 | 2019-06-05 |
| Simplifying Graph Convolutional Networks | ✓ Link | 64.86 ± 1.81 | SGC-1 | 2019-02-19 |
| Revisiting Heterophily For Graph Neural Networks | ✓ Link | 64.68 ± 2.85 | GCN+JK | 2022-10-14 |
| Semi-Supervised Classification with Graph Convolutional Networks | ✓ Link | 64.18 ± 2.62 | GCN | 2016-09-09 |
| Graph Attention Networks | ✓ Link | 63.9 ± 0.46 | GAT | 2017-10-30 |
| Revisiting Heterophily For Graph Neural Networks | ✓ Link | 63.68 ± 1.62 | ACM-SGC-1 | 2022-10-14 |
| Simple and Deep Graph Convolutional Networks | ✓ Link | 62.8 ± 2.87 | GCNII* | 2020-07-04 |
| Simplifying Graph Convolutional Networks | ✓ Link | 62.67 ± 2.41 | SGC-2 | 2019-02-19 |
| Inductive Representation Learning on Large Graphs | ✓ Link | 62.15 ± 0.42 | GraphSAGE | 2017-06-07 |
| Revisiting Heterophily For Graph Neural Networks | ✓ Link | 61.66 ± 2.29 | ACM-GCNII* | 2022-10-14 |
| Geom-GCN: Geometric Graph Convolutional Networks | ✓ Link | 60.9 | Geom-GCN* | 2020-02-13 |
| Revisiting Heterophily For Graph Neural Networks | ✓ Link | 60.48 ± 1.55 | ACM-SGC-2 | 2022-10-14 |
| Simple and Deep Graph Convolutional Networks | ✓ Link | 60.35 ± 2.7 | GCNII | 2020-07-04 |
| Revisiting Heterophily For Graph Neural Networks | ✓ Link | 58.73 ± 2.52 | ACM-GCNII | 2022-10-14 |
| Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Designs | ✓ Link | 52.30 ± 0.48 | H2GCN | 2020-06-20 |
| Predict then Propagate: Graph Neural Networks meet Personalized PageRank | ✓ Link | 51.91 ± 0.56 | APPNP | 2018-10-14 |
| Beyond Low-frequency Information in Graph Convolutional Networks | ✓ Link | 49.47 ± 2.84 | FAGCN | 2021-01-04 |
| Revisiting Heterophily For Graph Neural Networks | ✓ Link | 46.72 ± 0.46 | MLP-2 | 2022-10-14 |
| MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing | ✓ Link | 36.28 ± 10.22 | MixHop | 2019-04-30 |