paper-with-me

Node Classification 벤치마크

Node Classification on Squirrel (60%/20%/20% random splits)

37개 결과 · ⬇ CSV · JSON

1:1 Accuracy

24.55 37.84 51.14 64.43 77.72 2016-09 2026-11 GCN — 44.76 (2016-09-09) GraphSAGE — 41.26 (2017-06-07) GAT — 42.72 (2017-10-30) APPNP — 34.77 (2018-10-14) SGC-1 — 47.62 (2019-02-19) SGC-2 — 41.25 (2019-02-19) MixHop — 24.55 (2019-04-30) Snowball-3 — 48.25 (2019-06-05) Snowball-2 — 47.88 (2019-06-05) Geom-GCN* — 38.14 (2020-02-13) GPRGNN — 49.93 (2020-06-14) H2GCN — 30.39 (2020-06-20) GCNII — 38.81 (2020-07-04) GCNII* — 38.31 (2020-07-04) FAGCN — 42.24 (2021-01-04) BernNet — 51.35 (2021-06-21) ACMII-GCN++ — 69.98 (2022-10-14) ACMII-GCN+ — 69.81 (2022-10-14) ACM-GCN+ — 69.26 (2022-10-14) ACM-GCN++ — 68.56 (2022-10-14) ACM-Snowball-2 — 55.97 (2022-10-14) ACM-Snowball-3 — 55.73 (2022-10-14) ACMII-GCN — 54.53 (2022-10-14) ACMII-Snowball-2 — 53.48 (2022-10-14) GCN+JK — 53.4 (2022-10-14) ACMII-Snowball-3 — 52.31 (2022-10-14)  GAT+JK — 52.28 (2022-10-14) ACM-SGC-1 — 46.4 (2022-10-14) ACM-SGC-2 — 40.91 (2022-10-14) ACM-GCNII — 40.9 (2022-10-14) ACM-GCNII* — 38.32 (2022-10-14) MLP-2 — 31.28 (2022-10-14) NFGNN — 58.9 (2022-12-07) HH-GCN — 47.19 (2023-08-17) HH-GAT — 46.35 (2023-08-17) HH-GraphSAGE — 45.25 (2023-08-17) GNNDLD — 77.72 (2024-02-26) GCN — 44.76 (2016-09-09) SGC-1 — 47.62 (2019-02-19) Snowball-3 — 48.25 (2019-06-05) GPRGNN — 49.93 (2020-06-14) BernNet — 51.35 (2021-06-21) ACMII-GCN++ — 69.98 (2022-10-14) GNNDLD — 77.72 (2024-02-26)
RankModel 1:1 Accuracy PaperCodeYear
1 GNNDLD 77.72±0.84 GNNDLD: Graph Neural Network with Directional Label Distribution 2024
2 ACMII-GCN++ 69.98 ± 1.53 Revisiting Heterophily For Graph Neural Networks SitaoLuan/ACM-GNN 2022
3 ACMII-GCN+ 69.81 ± 1.11 Revisiting Heterophily For Graph Neural Networks SitaoLuan/ACM-GNN 2022
4 ACM-GCN+ 69.26 ± 1.11 Revisiting Heterophily For Graph Neural Networks SitaoLuan/ACM-GNN 2022
5 ACM-GCN++ 68.56 ± 1.33 Revisiting Heterophily For Graph Neural Networks SitaoLuan/ACM-GNN 2022
6 NFGNN 58.9±0.35 Node-oriented Spectral Filtering for Graph Neural Networks SsGood/NFGNN 2022
7 ACM-Snowball-2 55.97 ± 2.03 Revisiting Heterophily For Graph Neural Networks SitaoLuan/ACM-GNN 2022
8 ACM-Snowball-3 55.73 ± 2.39 Revisiting Heterophily For Graph Neural Networks SitaoLuan/ACM-GNN 2022
9 ACMII-GCN 54.53 ± 2.09 Revisiting Heterophily For Graph Neural Networks SitaoLuan/ACM-GNN 2022
10 ACMII-Snowball-2 53.48 ± 0.6 Revisiting Heterophily For Graph Neural Networks SitaoLuan/ACM-GNN 2022
11 GCN+JK 53.40 ± 1.90 Revisiting Heterophily For Graph Neural Networks SitaoLuan/ACM-GNN 2022
12 ACMII-Snowball-3 52.31 ± 1.57 Revisiting Heterophily For Graph Neural Networks SitaoLuan/ACM-GNN 2022
13  GAT+JK 52.28 ± 3.61 Revisiting Heterophily For Graph Neural Networks SitaoLuan/ACM-GNN 2022
14 BernNet 51.35 ± 0.73 BernNet: Learning Arbitrary Graph Spectral Filters via Bernstein Approximation ivam-he/BernNet 2021
15 GPRGNN 49.93 ± 0.53 Adaptive Universal Generalized PageRank Graph Neural Network jianhao2016/GPRGNN 2020
16 Snowball-3 48.25 ± 0.94 Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks PwnerHarry/Stronger_GCN 2019
17 Snowball-2 47.88 ± 1.23 Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks PwnerHarry/Stronger_GCN 2019
18 SGC-1 47.62 ± 1.27 Simplifying Graph Convolutional Networks dmlc/dgl · dmlc/dgl · dmlc/dgl · +4 2019
19 HH-GCN 47.19 ± 1.21 Half-Hop: A graph upsampling approach for slowing down message passing nerdslab/halfhop 2023
20 ACM-SGC-1 46.4 ± 1.13 Revisiting Heterophily For Graph Neural Networks SitaoLuan/ACM-GNN 2022
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