paper-with-me

Node Classification 벤치마크

Node Classification on Chameleon

61개 결과 · ⬇ CSV · JSON

Accuracy

42.73 52.17 61.61 71.04 80.48 2019-04 2026-09 MixHop — 60.5 (2019-04-30) Geom-GCN-P — 60.9 (2020-02-13) Geom-GCN-I — 60.31 (2020-02-13) Geom-GCN-S — 59.96 (2020-02-13) NLGCN  — 70.1 (2020-05-29) NLGAT  — 65.7 (2020-05-29) NLMLP  — 50.7 (2020-05-29) GPRGCN — 62.59 (2020-06-14) H2GCN-2 — 58.38 (2020-06-20) H2GCN-1 — 52.96 (2020-06-20) GCNII — 63.86 (2020-07-04) FAGCN — 46.07 (2021-01-04) GGCN — 71.14 (2021-02-12) FSGNN (8-hop) — 78.27 (2021-05-17) FSGNN (3-hop) — 78.14 (2021-05-17) WRGAT — 65.24 (2021-06-11) HLP Concat — 77.48 (2021-06-24) LW-GCN — 74.4 (2021-10-15) FDGATII — 65.1754 (2021-10-21) LINKX — 68.42 (2021-10-27) CNMPGNN — 73.29 (2021-11-15) SDRF — 42.73 (2021-11-29) Diag-NSD — 68.68 (2022-02-09) O(d)-NSD — 68.04 (2022-02-09) Gen-NSD — 67.93 (2022-02-09) GloGNN++ — 71.21 (2022-05-15) GloGNN — 69.78 (2022-05-15) UDGNN (GCN) — 74.53 (2022-05-30) LSC-ARMA — 68.4 (2022-06-06) Conn-NSD — 65.21 (2022-06-17) GCNII+DHGR — 74.57 (2022-09-17) ACMII-GCN++ — 74.76 (2022-10-14) ACMII-GCN+ — 74.56 (2022-10-14) ACM-GCN+ — 74.47 (2022-10-14) ACM-GCN++ — 74.41 (2022-10-14) ACM-GCN — 69.14 (2022-10-14) ACMII-GCN — 68.46 (2022-10-14) ACM-SGC-1 — 63.99 (2022-10-14) ACM-SGC-2 — 59.21 (2022-10-14) Graph ESN — 76.2 (2022-10-27) IIE-GNN — 72.13 (2022-11-20) Ordered GNN — 72.28 (2023-02-03) GCNH — 71.56 (2023-04-21) Dir-GNN — 79.71 (2023-05-17) SADE-GCN — 75.57 (2023-05-28) DJ-GNN — 80.48 (2023-06-29) UGT — 69.78 (2023-08-18) 2-HiGCN — 68.47 (2023-09-22) FaberNet — 80.33 (2023-10-03) SignGT — 74.31 (2023-10-17) ADPA — 46.2 (2023-12-07) CATv3-sup — 69.9 (2023-12-14) GraphSAGE-RARE (λ=1.0) — 69.28 (2023-12-15) LHS — 72.31 (2023-12-27) HiGNN — 68.86 (2024-03-26) M2M-GNN — 75.2 (2024-05-31) TE-GCNN — 71.14 (2024-06-08) RDGNN-I — 74.79 (2024-06-16) JKNet + Hetero-S (8 layers) — 70.18 (2024-06-18) CoED — 79.69 (2024-10-18) Gprompt+CausalMP — 59.14 (2024-11-21) MixHop — 60.5 (2019-04-30) Geom-GCN-P — 60.9 (2020-02-13) NLGCN  — 70.1 (2020-05-29) GGCN — 71.14 (2021-02-12) FSGNN (8-hop) — 78.27 (2021-05-17) Dir-GNN — 79.71 (2023-05-17) DJ-GNN — 80.48 (2023-06-29)
RankModel Accuracy PaperCodeYear
1 DJ-GNN 80.48±1.46 Diffusion-Jump GNNs: Homophiliation via Learnable Metric Filters AhmedBegggaUA/TFM 2023
2 FaberNet 80.33±1.19 HoloNets: Spectral Convolutions do extend to Directed Graphs ChristianKoke/HoloNets 2023
3 Dir-GNN 79.71±1.26 Edge Directionality Improves Learning on Heterophilic Graphs emalgorithm/directed-graph-neural-network 2023
4 CoED 79.69±1.35 Improving Graph Neural Networks by Learning Continuous Edge Directions hormoz-lab/coed-gnn 2024
5 FSGNN (8-hop) 78.27±1.28 Improving Graph Neural Networks with Simple Architecture Design sunilkmaurya/FSGNN 2021
6 FSGNN (3-hop) 78.14±1.25 Improving Graph Neural Networks with Simple Architecture Design sunilkmaurya/FSGNN 2021
7 HLP Concat 77.48±0.80 Simple Truncated SVD based Model for Node Classification on Heterophilic Graphs 2021
8 Graph ESN 76.2±1.2 Beyond Homophily with Graph Echo State Networks 2022
9 SADE-GCN 75.57±1.57 Self-attention Dual Embedding for Graphs with Heterophily 2023
10 M2M-GNN 75.20 ± 2.3 Sign is Not a Remedy: Multiset-to-Multiset Message Passing for Learning on Heterophilic Graphs Jinx-byebye/m2mgnn 2024
11 RDGNN-I 74.79 ± 2.14 Graph Neural Reaction Diffusion Models 2024
12 ACMII-GCN++ 74.76 ± 2.2 Revisiting Heterophily For Graph Neural Networks SitaoLuan/ACM-GNN 2022
13 GCNII+DHGR 74.57±2.56 Make Heterophily Graphs Better Fit GNN: A Graph Rewiring Approach 2022
14 ACMII-GCN+ 74.56 ± 2.08 Revisiting Heterophily For Graph Neural Networks SitaoLuan/ACM-GNN 2022
15 UDGNN (GCN) 74.53±1.19 Universal Deep GNNs: Rethinking Residual Connection in GNNs from a Path Decomposition Perspective for Preventing the Over-smoothing 2022
16 ACM-GCN+ 74.47 ± 1.84 Revisiting Heterophily For Graph Neural Networks SitaoLuan/ACM-GNN 2022
17 ACM-GCN++ 74.41 ± 1.49 Revisiting Heterophily For Graph Neural Networks SitaoLuan/ACM-GNN 2022
18 LW-GCN 74.4±1.4 Label-Wise Graph Convolutional Network for Heterophilic Graphs enyandai/lwgcn 2021
19 SignGT 74.31±1.24 SignGT: Signed Attention-based Graph Transformer for Graph Representation Learning 2023
20 CNMPGNN 73.29±1.29 CN-Motifs Perceptive Graph Neural Networks 2021
1–20 / 61 다음 → 페이지당 10 20 50 100