paper-with-me

Node Classification 벤치마크

Node Classification on Cora Full-supervised

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Accuracy

61.8 68.47 75.14 81.82 88.49 2017-06 2026-09 GraphSAGE — 82.2 (2017-06-07) FastGCN — 85.0 (2018-01-30) ASGCN — 87.44 (2018-09-14) IncepGCN+DropEdge — 88.2 (2019-07-25) GraphMix (GCN) — 61.8 (2019-09-25) GCNII — 88.49 (2020-07-04) FDGATII — 87.7867 (2021-10-21) Graph ESN — 86.0 (2022-10-27) NCGCN — 73.42 (2023-06-04) GraphSAGE — 82.2 (2017-06-07) FastGCN — 85.0 (2018-01-30) ASGCN — 87.44 (2018-09-14) IncepGCN+DropEdge — 88.2 (2019-07-25) GCNII — 88.49 (2020-07-04)
RankModel Accuracy PaperCodeYear
1 GCNII 88.49% Simple and Deep Graph Convolutional Networks chennnM/GCNII · chennnM/GCNII · zhanglab-aim/cancer-net · +1 2020
2 IncepGCN+DropEdge 88.2% DropEdge: Towards Deep Graph Convolutional Networks on Node Classification GraphSAINT/GraphSAINT · GraphSAINT/GraphSAINT · DropEdge/DropEdge · +4 2019
3 FDGATII 87.7867% FDGATII : Fast Dynamic Graph Attention with Initial Residual and Identity Mapping gayanku/FDGATII 2021
4 ASGCN 87.44±0.0034% Adaptive Sampling Towards Fast Graph Representation Learning dmlc/dgl · huangwb/AS-GCN 2018
5 Graph ESN 86.0±1.0 Beyond Homophily with Graph Echo State Networks 2022
6 FastGCN 85.00% FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling matenure/FastGCN · gkunnan97/fastgcn_pytorch · jiechenjiechen/FastGCN-matlab · +1 2018
7 GraphSAGE 82.2% Inductive Representation Learning on Large Graphs pyg-team/pytorch_geometric · dmlc/dgl · dmlc/dgl · +17 2017
8 NCGCN 73.42 ± 0.58% Clarify Confused Nodes via Separated Learning GISec-Team/NCGNN 2023
9 GraphMix (GCN) 61.8% GraphMix: Improved Training of GNNs for Semi-Supervised Learning vikasverma1077/GraphMix 2019
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