paper-with-me

Node Classification 벤치마크

Node Classification on USA Air-Traffic

7개 결과 · ⬇ CSV · JSON

Accuracy

31.6 40.26 48.91 57.56 66.22 2016-03 2026-09 Planetoid* — 64.7 (2016-03-29) GraphSAGE (Hamilton et al., [2017a]) — 31.6 (2017-06-07) GAT (Velickovic et al., 2018) — 58.5 (2017-10-30) Union (Li et al., 2018) — 58.2 (2018-01-22) Intersection (Li et al., 2018) — 57.3 (2018-01-22) DEMO-Net(weight) — 64.7 (2019-06-05) UGT — 66.22 (2023-08-18) Planetoid* — 64.7 (2016-03-29) UGT — 66.22 (2023-08-18)
RankModel Accuracy PaperCodeYear
1 UGT 66.22±4.55 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
2 DEMO-Net(weight) 64.7 DEMO-Net: Degree-specific Graph Neural Networks for Node and Graph Classification jwu4sml/DEMO-Net 2019
2 Planetoid* 64.7 Revisiting Semi-Supervised Learning with Graph Embeddings tkipf/gcn · kimiyoung/planetoid · DeepGraphLearning/GMNN · +23 2016
4 GAT (Velickovic et al., 2018) 58.5 Graph Attention Networks labmlai/annotated_deep_learning_paper_implementations · dmlc/dgl · dmlc/dgl · +90 2017
5 Union (Li et al., 2018) 58.2 Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning liqimai/gcn 2018
6 Intersection (Li et al., 2018) 57.3 Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning liqimai/gcn 2018
7 GraphSAGE (Hamilton et al., [2017a]) 31.6 Inductive Representation Learning on Large Graphs pyg-team/pytorch_geometric · dmlc/dgl · dmlc/dgl · +17 2017
1–7 / 7 페이지당 10 20 50 100