SCR: Training Graph Neural Networks with Consistency Regularization
SCR：利用一致性正则化训练图神经网络AI 解读 ↓
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SCR：利用一致性正则化训练图神经网络
- 研究问题
- 半监督图节点分类中，如何平衡标注与未标注数据的训练误差，设计能改善图神经网络泛化的一致性正则化？
- 方法与思路
- 作者提出 SCR 框架，分别约束扰动模型预测的一致性，或利用 Mean Teacher 范式构造教师与学生模型的一致性损失。
- 材料陈述的贡献
- 摘要称，SCR 在三个大型 OGB 节点分类数据集上改善多种 GNN，且在该稿提交时位居三个对应榜单首位。
- 阅读关注点
- 应核对扰动方式、损失权重、教师更新、数据划分及基线调参，并检查实验增益和排行榜记录对应的版本日期。
- 阅读边界
- 所据为 arXiv 预印本，同行评审与期刊接收未核实；榜单名次仅为提交时陈述，摘要未给出数值或跨场景泛化证据。
原文摘要
We present the SCR framework for enhancing the training of graph neural networks (GNNs) with consistency regularization. Regularization is a set of strategies used in Machine Learning to reduce overfitting and improve the generalization ability. However, it is unclear how to best design the generalization strategies in GNNs, as it works in a semi-supervised setting for graph data. The major challenge lies in how to efficiently balance the trade-off between the error from the labeled data and that from the unlabeled data. SCR is a simple yet general framework in which we introduce two strategies of consistency regularization to address the challenge above. One is to minimize the disagreements among the perturbed predictions by different versions of a GNN model. The other is to leverage the Mean Teacher paradigm to estimate a consistency loss between teacher and student models instead of the disagreement of the predictions. We conducted experiments on three large-scale node classification datasets in the Open Graph Benchmark (OGB). Experimental results demonstrate that the proposed SCR framework is a general one that can enhance various GNNs to achieve better performance. Finally, SCR has been the top-1 entry on all three OGB leaderboards as of this submission.