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Node Classification 벤치마크

Node Classification on Squirrel

59개 결과 · ⬇ CSV · JSON

Accuracy

28.98 40.91 52.84 64.78 76.71 2019-04 2026-11 MixHop — 43.8 (2019-04-30) Geom-GCN-P — 38.14 (2020-02-13) Geom-GCN-S — 36.24 (2020-02-13) Geom-GCN-I — 33.32 (2020-02-13) NLGCN  — 59.0 (2020-05-29) NLGAT  — 56.8 (2020-05-29) NLMLP  — 33.7 (2020-05-29) GPRGCN — 46.31 (2020-06-14) H2GCN-2 — 32.33 (2020-06-20) H2GCN-1 — 28.98 (2020-06-20) GCNII — 38.47 (2020-07-04) FAGCN — 30.83 (2021-01-04) GGCN — 55.17 (2021-02-12) FSGNN (8-hop) — 74.1 (2021-05-17) WRGAT — 48.85 (2021-06-11) HLP Concat — 74.17 (2021-06-24) LW-GCN — 62.6 (2021-10-15) LINKX — 61.81 (2021-10-27) CNMPGNN — 63.6 (2021-11-15) SDRF — 37.05 (2021-11-29) O(d)-NSD — 56.34 (2022-02-09) Diag-NSD — 54.78 (2022-02-09) Gen-NSD — 53.17 (2022-02-09) GloGNN++ — 57.88 (2022-05-15) GloGNN — 57.54 (2022-05-15) UDGNN (GCN) — 68.13 (2022-05-30) LCS-GAT — 56.3 (2022-06-06) Conn-NSD — 45.19 (2022-06-17) H2GCN+DHGR — 72.24 (2022-09-17) ACMII-GCN++ — 67.4 (2022-10-14) ACMII-GCN+ — 67.07 (2022-10-14) ACM-GCN++ — 67.06 (2022-10-14) ACM-GCN+ — 66.98 (2022-10-14) ACM-GCN — 55.19 (2022-10-14) ACMII-GCN — 51.8 (2022-10-14) ACM-SGC-1 — 45.0 (2022-10-14) ACM-SGC-2 — 40.02 (2022-10-14) Graph ESN — 71.2 (2022-10-27) IIE-GNN — 57.32 (2022-11-20) Ordered GNN — 62.44 (2023-02-03) GCNH — 61.85 (2023-04-21) Dir-GNN — 75.31 (2023-05-17) SADE-GCN — 68.2 (2023-05-28) DJ-GNN — 73.48 (2023-06-29) UGT — 66.96 (2023-08-18) FaberNet — 76.71 (2023-10-03) SignGT — 64.25 (2023-10-17) ADPA — 45.2 (2023-12-07) CATv3-sup — 59.3 (2023-12-14) GCN-RARE (λ=1.0) — 55.9 (2023-12-15) LHS — 60.27 (2023-12-27) HiGNN — 54.78 (2024-03-26) M2M-GNN — 63.6 (2024-05-31) HDP — 62.07 (2024-05-31) TE-GCNN — 55.04 (2024-06-08) RDGNN-I — 65.62 (2024-06-16) JKNet + Hetero-S (8 layers) — 57.83 (2024-06-18) CoED — 75.32 (2024-10-18) Gprompt+CausalMP — 39.78 (2024-11-21) MixHop — 43.8 (2019-04-30) NLGCN  — 59.0 (2020-05-29) FSGNN (8-hop) — 74.1 (2021-05-17) HLP Concat — 74.17 (2021-06-24) Dir-GNN — 75.31 (2023-05-17) FaberNet — 76.71 (2023-10-03)
RankModel Accuracy PaperCodeYear
1 FaberNet 76.71±1.92 HoloNets: Spectral Convolutions do extend to Directed Graphs ChristianKoke/HoloNets 2023
2 CoED 75.32±1.82 Improving Graph Neural Networks by Learning Continuous Edge Directions hormoz-lab/coed-gnn 2024
3 Dir-GNN 75.31±1.92 Edge Directionality Improves Learning on Heterophilic Graphs emalgorithm/directed-graph-neural-network 2023
4 HLP Concat 74.17±1.83 Simple Truncated SVD based Model for Node Classification on Heterophilic Graphs 2021
5 FSGNN (8-hop) 74.10±1.89 Improving Graph Neural Networks with Simple Architecture Design sunilkmaurya/FSGNN 2021
6 DJ-GNN 73.48±1.59 Diffusion-Jump GNNs: Homophiliation via Learnable Metric Filters AhmedBegggaUA/TFM 2023
7 H2GCN+DHGR 72.24±1.52 Make Heterophily Graphs Better Fit GNN: A Graph Rewiring Approach 2022
8 Graph ESN 71.2±1.5 Beyond Homophily with Graph Echo State Networks 2022
9 SADE-GCN 68.20±1.57 Self-attention Dual Embedding for Graphs with Heterophily 2023
10 UDGNN (GCN) 68.13±2.59 Universal Deep GNNs: Rethinking Residual Connection in GNNs from a Path Decomposition Perspective for Preventing the Over-smoothing 2022
11 ACMII-GCN++ 67.4 ± 2.21 Revisiting Heterophily For Graph Neural Networks SitaoLuan/ACM-GNN 2022
12 ACMII-GCN+ 67.07 ± 1.65 Revisiting Heterophily For Graph Neural Networks SitaoLuan/ACM-GNN 2022
13 ACM-GCN++ 67.06 ± 1.66 Revisiting Heterophily For Graph Neural Networks SitaoLuan/ACM-GNN 2022
14 ACM-GCN+ 66.98 ± 1.71 Revisiting Heterophily For Graph Neural Networks SitaoLuan/ACM-GNN 2022
15 UGT 66.96 ±2.49 Transitivity-Preserving Graph Representation Learning for Bridging Local Connectivity and Role-based Similarity nslab-cuk/unified-graph-transformer · nslab-cuk/community-aware-graph-transformer · nslab-cuk/literalkg 2023
16 RDGNN-I 65.62 ± 2.33 Graph Neural Reaction Diffusion Models 2024
17 SignGT 64.25±1.48 SignGT: Signed Attention-based Graph Transformer for Graph Representation Learning 2023
18 CNMPGNN 63.60±1.96 CN-Motifs Perceptive Graph Neural Networks 2021
19 M2M-GNN 63.60 ± 1.7 Sign is Not a Remedy: Multiset-to-Multiset Message Passing for Learning on Heterophilic Graphs Jinx-byebye/m2mgnn 2024
20 LW-GCN 62.6±1.6 Label-Wise Graph Convolutional Network for Heterophilic Graphs enyandai/lwgcn 2021
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