paper-with-me

Node Classification 벤치마크

Node Classification on PATTERN 100k

9개 결과 · ⬇ CSV · JSON

Accuracy (%)

50.52 59.59 68.67 77.74 86.82 2016-11 2026-11 MoNet — 85.482 (2016-11-25) GraphSage — 50.516 (2017-06-07) GAT — 75.824 (2017-10-30) GatedGCN — 84.48 (2017-11-20) GIN — 85.59 (2018-10-01) PNA — 86.567 (2020-04-12) DGN — 86.68 (2020-10-06) FactorGCN — 86.57 (2020-10-12) EGT — 86.816 (2021-08-07) MoNet — 85.482 (2016-11-25) GIN — 85.59 (2018-10-01) PNA — 86.567 (2020-04-12) DGN — 86.68 (2020-10-06) EGT — 86.816 (2021-08-07)
RankModel Accuracy (%) PaperCodeYear
1 EGT 86.816 Global Self-Attention as a Replacement for Graph Convolution shamim-hussain/egt_pytorch · shamim-hussain/egt · shamim-hussain/egt_triangular 2021
2 DGN 86.680 Directional Graph Networks Saro00/DGN · gbouritsas/gsn 2020
3 FactorGCN 86.57 ± 0.02 Factorizable Graph Convolutional Networks ihollywhy/FactorGCN.PyTorch 2020
4 PNA 86.567 Principal Neighbourhood Aggregation for Graph Nets rusty1s/pytorch_geometric · dmlc/dgl · lukecavabarrett/pna · +5 2020
5 GIN 85.590 How Powerful are Graph Neural Networks? dmlc/dgl · dmlc/dgl · weihua916/powerful-gnns · +16 2018
6 MoNet 85.482 Geometric deep learning on graphs and manifolds using mixture model CNNs dmlc/dgl · dmlc/dgl · theswgong/MoNet · +1 2016
7 GatedGCN 84.480 Residual Gated Graph ConvNets xbresson/spatial_graph_convnets 2017
8 GAT 75.824 Graph Attention Networks labmlai/annotated_deep_learning_paper_implementations · dmlc/dgl · dmlc/dgl · +90 2017
9 GraphSage 50.516 Inductive Representation Learning on Large Graphs pyg-team/pytorch_geometric · dmlc/dgl · dmlc/dgl · +17 2017
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