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Node Classification on Non-Homophilic (Heterophilic) Graphs 벤치마크

Node Classification on Non-Homophilic (Heterophilic) Graphs on Cornell (60%/20%/20% random splits)

33개 결과 · ⬇ CSV · JSON

1:1 Accuracy

60.33 69.22 78.12 87.01 95.9 2016-09 2026-09 GCN — 82.46 (2016-09-09) GraphSAGE — 71.41 (2017-06-07) GAT — 76.0 (2017-10-30) APPNP — 91.8 (2018-10-14) SGC-2 — 72.62 (2019-02-19) SGC-1 — 70.98 (2019-02-19) MixHop — 60.33 (2019-04-30) Snowball-3 — 82.95 (2019-06-05) Snowball-2 — 82.62 (2019-06-05) Geom-GCN* — 60.81 (2020-02-13) GPRGNN — 91.36 (2020-06-14) MLP-2 — 91.3 (2020-06-14) H2GCN — 86.23 (2020-06-20) GCNII* — 90.49 (2020-07-04) GCNII — 89.18 (2020-07-04) FAGCN — 88.03 (2021-01-04) BernNet — 92.13 (2021-06-21) ACMII-GCN — 95.9 (2022-10-14) ACMII-Snowball-2 — 95.25 (2022-10-14) ACM-Snowball-2 — 95.08 (2022-10-14) ACM-GCN+ — 94.92 (2022-10-14) ACM-GCN — 94.75 (2022-10-14) ACM-Snowball-3 — 94.26 (2022-10-14) ACM-GCN++ — 93.93 (2022-10-14) ACMII-GCN+ — 93.93 (2022-10-14) ACM-SGC-2 — 93.77 (2022-10-14) ACM-SGC-1 — 93.77 (2022-10-14) ACMII-Snowball-3 — 93.61 (2022-10-14) ACM-GCNII* — 93.44 (2022-10-14) ACMII-GCN++ — 92.62 (2022-10-14) ACM-GCNII — 92.62 (2022-10-14) GAT+JK — 74.43 (2022-10-14) GCN+JK — 66.56 (2022-10-14) GCN — 82.46 (2016-09-09) APPNP — 91.8 (2018-10-14) BernNet — 92.13 (2021-06-21) ACMII-GCN — 95.9 (2022-10-14)
RankModel 1:1 Accuracy PaperCodeYear
1 ACMII-GCN 95.9 ± 1.83 Revisiting Heterophily For Graph Neural Networks SitaoLuan/ACM-GNN 2022
2 ACMII-Snowball-2 95.25 ± 1.55 Revisiting Heterophily For Graph Neural Networks SitaoLuan/ACM-GNN 2022
3 ACM-Snowball-2 95.08 ± 3.11 Revisiting Heterophily For Graph Neural Networks SitaoLuan/ACM-GNN 2022
4 ACM-GCN+ 94.92 ± 2.79 Revisiting Heterophily For Graph Neural Networks SitaoLuan/ACM-GNN 2022
5 ACM-GCN 94.75 ± 3.8 Revisiting Heterophily For Graph Neural Networks SitaoLuan/ACM-GNN 2022
6 ACM-Snowball-3 94.26 ± 2.57 Revisiting Heterophily For Graph Neural Networks SitaoLuan/ACM-GNN 2022
7 ACM-GCN++ 93.93 ± 1.05 Revisiting Heterophily For Graph Neural Networks SitaoLuan/ACM-GNN 2022
8 ACMII-GCN+ 93.93 ± 3.03 Revisiting Heterophily For Graph Neural Networks SitaoLuan/ACM-GNN 2022
9 ACM-SGC-2 93.77 ± 2.17 Revisiting Heterophily For Graph Neural Networks SitaoLuan/ACM-GNN 2022
10 ACM-SGC-1 93.77 ± 1.91 Revisiting Heterophily For Graph Neural Networks SitaoLuan/ACM-GNN 2022
11 ACMII-Snowball-3 93.61 ± 2.79 Revisiting Heterophily For Graph Neural Networks SitaoLuan/ACM-GNN 2022
12 ACM-GCNII* 93.44 ± 2.74 Revisiting Heterophily For Graph Neural Networks SitaoLuan/ACM-GNN 2022
13 ACMII-GCN++ 92.62 ± 2.57 Revisiting Heterophily For Graph Neural Networks SitaoLuan/ACM-GNN 2022
14 ACM-GCNII 92.62 ± 3.13 Revisiting Heterophily For Graph Neural Networks SitaoLuan/ACM-GNN 2022
15 BernNet 92.13 ± 1.64 BernNet: Learning Arbitrary Graph Spectral Filters via Bernstein Approximation ivam-he/BernNet 2021
16 APPNP 91.80 ± 0.63 Predict then Propagate: Graph Neural Networks meet Personalized PageRank dmlc/dgl · dmlc/dgl · benedekrozemberczki/APPNP · +2 2018
17 GPRGNN 91.36 ± 0.70 Adaptive Universal Generalized PageRank Graph Neural Network jianhao2016/GPRGNN 2020
18 MLP-2 91.30 ± 0.70 Adaptive Universal Generalized PageRank Graph Neural Network jianhao2016/GPRGNN 2020
19 GCNII* 90.49 ± 4.45 Simple and Deep Graph Convolutional Networks chennnM/GCNII · chennnM/GCNII · zhanglab-aim/cancer-net · +1 2020
20 GCNII 89.18 ± 3.96 Simple and Deep Graph Convolutional Networks chennnM/GCNII · chennnM/GCNII · zhanglab-aim/cancer-net · +1 2020
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