基于无负样本损失和自适应增强的图对比学习

周天琪,杨艳,张继杰,殷少伟,郭增强

Graph contrastive learning based on negative-sample-free loss and adaptive augmentation

Tian-qi ZHOU,Yan YANG,Ji-jie ZHANG,Shao-wei YIN,Zeng-qiang GUO

表 3 节点分类准确率的比较分析

Tab.3 Comparison and analysis of node classification accuracy

% 模型 输入数据 Cora Citeseer Pubmed DeepWalk ${\boldsymbol{A}}$ 67.2 43.2 63.0 Raw features ${\boldsymbol{X}}$ 47.9 ± 0.4 49.3 ± 0.2 69.1 ± 0.2 LP ${\boldsymbol{A}}$, ${\boldsymbol{Y}}$ 68.0 45.3 63.0 MLP ${\boldsymbol{X}}$, ${\boldsymbol{Y}}$ 55.1 46.5 71.4 PLANETOID ${\boldsymbol{X}}$, ${\boldsymbol{Y}}$ 75.7 64.7 77.2 GraphSAGE ${\boldsymbol{A}}$, ${\boldsymbol{X}}$, ${\boldsymbol{Y}}$ 79.2 ± 0.5 71.2 ± 0.5 73.1 ± 1.4 Chebyshev ${\boldsymbol{A}}$, ${\boldsymbol{X}}$, ${\boldsymbol{Y}}$ 81.2 69.8 74.4 GCN ${\boldsymbol{A}}$, ${\boldsymbol{X}}$, ${\boldsymbol{Y}}$ 81.5 70.3 79.0 GAT ${\boldsymbol{A}}$, ${\boldsymbol{X}}$, ${\boldsymbol{Y}}$ 83.0 ± 0.7 72.5 ± 0.7 79.0 ± 0.3 DeepWalk-F ${\boldsymbol{A}}$, ${\boldsymbol{X}}$ 70.7 ± 0.6 51.4 ± 0.5 74.3 ± 0.9 Unsup-GraphSAGE ${\boldsymbol{A}}$, ${\boldsymbol{X}}$ 75.2 ± 1.5 59.4 ± 0.9 70.1 ± 1.4 DGI ${\boldsymbol{A}}$, ${\boldsymbol{X}}$ 82.3 ± 0.6 71.8 ± 0.7 76.9 ± 0.6 GMI ${\boldsymbol{A}}$, ${\boldsymbol{X}}$ 82.8 ± 0.3 72.3 ± 0.3 79.8 ± 0.2 GRACE ${\boldsymbol{A}}$, ${\boldsymbol{X}}$ 80.0 ± 0.4 71.7 ± 0.6 79.5 ± 1.1 GCA ${\boldsymbol{A}}$, ${\boldsymbol{X}}$ 80.5 ± 0.5 71.3 ± 0.4 78.6 ± 0.6 CG3 ${\boldsymbol{A}}$, ${\boldsymbol{X}}$ 83.1 ±0.6 73.0 ± 0.5 80.2±0.7 GNSA ${\boldsymbol{A}}$,${\boldsymbol{X}}$ 83.3 ± 0.7 72.6 ± 0.1 81.6 ± 0.1