Node Classification in Graph Neural Networks under Dual Consistency

Qiyu Li, Xianxian Li, De Li, Jinyan Wang

Abstract: In recent years, graph neural networks (GNNs) have become a popular semi-supervised learning method for processing graph-structured data. However, traditional graph neural network models rely heavily on labeled data during learning while neglecting the potential of unlabeled data. To further exploit unlabeled data, pseudo-labeling-based methods select high-confidence pseudo-labeling during training and assign unlabeled nodes to join the training accordingly. Unfortunately, there is a confidence bias in the training process with this approach. On the other hand, self-supervised learning trains GNNs models to learn embeddings by mining the supervised information in the data. Since the training process does not introduce labeling information, the discriminative information of classes is not learned. To this end, for node classification in GNNs we propose a novel method that combines self-supervised and semi-supervised approaches, namely SLCSL, which mainly consists of two modules, random neighbors node consistency and class-centered consistency. Random neighbors node consistency explores rich node representations in unlabeled data by aligning random neighbor nodes. To make full use of labeled information, class-centered consistency establishes relationships between labeled and unlabeled nodes, thus enabling unlabeled nodes to be better aware of class information. Finally, experiments are conducted on various real-world datasets, and the results show that SLCSL outperforms the current state-of-the-art methods.