Learning Augmentation for GNNs With Consistency Regularization

IEEE Access

Dasol Hwang¹  Seunghun Lee¹  Hyeonjin Park¹  Hyunwoo J. Kim¹  Jung-Woo Ha²  Jisu Jeong²  Kyung-Min Kim² 

[1] Department of Computer Science and Engineering, Korea University, Seoul, Republic of Korea;Naver AI Laboratory, Seongnam, Republic of Korea;

关键词: Graph neural networks;    augmentation;    semi-supervised learning;    meta-learning;   

DOI  :  10.1109/ACCESS.2021.3111908

来源: DOAJ

【 摘 要 】

Graph neural networks (GNNs) have demonstrated superior performance in various tasks on graphs. However, existing GNNs often suffer from weak-generalization due to sparsely labeled datasets. Here we propose a novel framework that learns to augment the input features using topological information and automatically controls the strength of augmentation. Our framework learns the augmentor to minimize GNNs’ loss on unseen labeled data while maximizing the consistency of GNNs’ predictions on unlabeled data. This can be formulated as a meta-learning problem and our framework alternately optimizes the augmentor and GNNs for a target task. Our extensive experiments demonstrate that the proposed framework is applicable to any GNNs and significantly improves the performance of graph neural networks on node classification. In particular, our method provides 5.78% improvement with Graph convolutional network (GCN) on average across five benchmark datasets.

【 授权许可】

Unknown