Semi-supervised node classification via fine-grained graph auxiliary augmentation learning. (May 2023)

• Record Type: Journal Article
• Authors: Lv, Jia; Song, Kaikai; Ye, Qiang; Tian, Guangjian
• Is Part Of: Pattern recognition. Volume 137(2023)
• Publication Date: 2023-05
• Subjects: Graph neural network -- Node classification -- Data augmentation -- Auxiliary learning; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2023.109301
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: A novel framework named graph auxiliary augmentation learning (GAU) is proposed, which co-trains the primary task together with a fine-grained auxiliary classification through a multi-task GNN. It alleviates the sensitivity of the model to the pseudo-label quality and reduces the model degradation due to the accumulative error of the pseudo-labels. The fine-grained auxiliary classification task helps to learn better node representations from a different view, thereby boosting the performance of the primary task. It is architecture-agnostic so that it can be applied to any variant of GNN. Experiments show that it can achieve superior performance on different architectures when compared with other state-of-the-art methods. Abstract: Node classification has become an important research topic in recent years. Since there are always a few training samples, researchers improve the performance by properly leveraging the predictions of unlabeled nodes during training. However, suffering from the model degradation resulted from the accumulative error of pseudo-labels, there is limited improvement. In this paper we present fine-grained G raph A uxiliary aU gmentation (GAU). It trains the primary task together with an automatically created auxiliary task which is a fine-grained node classification task. And an auxiliary augmentation strategy is designed to enlarge the labeled set for the auxiliary task by utilizing the pseudo-labels of the primary task. Comprehensive experiments show that GAU alleviates the sensitivity of the model to the pseudo-label quality, so more unlabeled nodes can participate in the training. From the perspective of co-training, the fine-grained auxiliary task which is trained by much more unlabeled nodes helps to learn better node representations from a different view, thereby boosting the final performance. Extensive experiments verify the superior performance of the GAU on different GNN architectures when compared with other state-of-the-art approaches.