效果测试

• PPI
• Reddit

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