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| Imbalanced Node Classification Algorithm Based on Self-Supervised Learning |
| CUI Caixia1,2, WANG Jie3, PANG Tianjie2, LIANG Jiye1 |
1. School of Computer and Information Technology, Shanxi University, Taiyuan 030006;
2. College of Computer Science and Technology, Taiyuan Normal University, Jinzhong 030619;
3. College of Computer Science and Technology, Taiyuan University of Science and Technology, Taiyuan 030024 |
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Abstract In real-world node classification scenarios, only a few nodes are labeled and their class labels are imbalanced. In most of the existing methods, the lack of the supervision information and the imbalance of node classes are not taken into account at the same time, and the improvement of node classification performance cannot be guaranteed. Therefore, an imbalanced node classification algorithm based on self-supervised learning is proposed. Firstly, different views of the original graph are generated through graph data augmentation. Then, node representations are learned by maximizing the consistency of node representations across views using self-supervised learning. The supervised information is expanded and the expressive ability of nodes is enhanced by self-supervised learning. In addition, a semantic constraint loss is designed to ensure semantic consistency in graph data augmentation along with cross-entropy loss and self-supervised contrastive loss. Experimental results on three real graph datasets show that the proposed algorithm achieves better performance on solving the imbalanced node classification problem.
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Received: 23 August 2022
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| Fund:National Natural Science Foundation of China(No.61976184,62272285) |
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Corresponding Authors:
LIANG Jiye, Ph.D., professor. His research interests include data mining, machine learning, big data analysis and artificial intelligence.
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| About author:: CUI Caixia, Ph.D. candidate. Her research interests include data mining and machine learning. WANG Jie, Ph.D., lecturer. His research interests include data mining and machine learning.PANG Tianjie, master, associate profe-ssor. His research interests include data mi-ning and machine learning. |
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2022, Vol. 35 
