2021/12/08 by Chenhui Zhang, Zhang, Chenhui, Yufei He +17 · 1 citation
Computer Science · Mathematics · #Advanced Graph Neural Networks #Advanced Neural Network Applications #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and ELM #Social and Information Networks (cs.SI) #cs.LG #cs.SI #stat.ML
paper · pdf · doi:10.48550/arxiv.2112.04319
openalex publication_date 2021/12/08 · arxiv created 2022/06/13 · arxiv updated 2022/06/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.