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效果测试

Yi Ren edited this page Jan 17, 2019 · 8 revisions

本节描述Euler算法包在PPI和Reddit两个数据集上的模型调优效果。所有模型的embedding大小均为256。训练的batch size均为512。

GraphSAGE等邻居汇聚算法既可以采用supervised训练方式,也可以用unsupervised训练模式。我们用后缀加以区分。LINE/DeepWalk等算法则仅应用无监督训练模式。

  • 在有监督训练模式下,我们直接预测标注信息。除GAT外,训练epoch数为20。GAT为对齐论文的配置训练100epoch。
  • 非监督模式下,我们先进行20个epoch训练以生成节点的embedding向量。然后此embedding作为Logistic Regression模型的特征进行20个epoch的supervised训练。

下表所列指标均为测试集的micro-F1。

PPI

模型 论文汇报 F1 Euler F1 备注
Random 0.396 0.415
DeepWalk NA 0.536
LINE-1stOrder NA 0.517 opt = sgd / lr = 2e-1
LINE-2ndOrder NA 0.535 opt = sgd / lr = 2e-1
GraphSage-GCN 0.465 0.460 opt = adam / lr = 2e-3
GraphSage-Mean 0.486 0.502 opt = adam / lr = 1e-3
GraphSage-Meanpool NA 0.486 opt = adam / lr = 1e-3
GraphSage-Maxpool 0.502 0.489 opt = adam / lr = 1e-3
GraphSage-GCN-Supervised 0.500 0.504 opt = adam / lr = 1e-2
GraphSage-Mean-Supervised 0.598 0.614 opt = adam / lr = 1e-2
GraphSage-Meanpool-Supervised NA 0.640 opt = adam / lr = 5e-3
GraphSage-Maxpool-Supervised 0.600 0.634 opt = adam / lr = 5e-3
ScalableGCN-Mean-Supervised NA 0.603 opt = adam / lr = 2e-1 / store lr = 2e-3
ScalableGCN-Meanpool-Supervised NA 0.606 opt = adam / lr = 5e-3 / store lr = 5e-4
GAT 0.973 0.948 opt = adam / lr = 5e-3 / head_num=4 / layer_num=3 / sample_neighbor=150

Reddit

模型 论文汇报 F1 Euler F1 备注
Random 0.043 0.120
DeepWalk NA 0.841
LINE-1stOrder NA 0.813 opt = sgd / lr = 2e-1
LINE-2ndOrder NA 0.820 opt = sgd / lr = 2e-1
GraphSage-GCN-Supervised 0.930 0.917 opt = adam / lr = 1e-2
GraphSage-Mean-Supervised 0.950 0.933 opt = adam / lr = 1e-2
GraphSage-Meanpool-Supervised NA 0.928 opt = adam / lr = 5e-3
ScalableGCN-Mean-Supervised NA 0.929 opt = adam / lr = 1e-2 / store lr = 2e-3
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