Information Augmentation for Few-shot Node Classification

Although meta-learning and metric learning have been widely applied for few-shot node classification (FSNC), some limitations still need to be addressed, such as expensive time costs for the meta-train and difficult of exploring the complex structure inherent the graph data. To address in issues, this paper proposes a new data augmentation method to conduct FSNC on the graph data including parameter initialization and parameter fine-tuning. Specifically, parameter initialization only conducts a multi-classification task on the base classes, resulting in good generalization ability and less time cost. Parameter fine-tuning designs two data augmentation methods (i. e. , support augmentation and shot augmentation) on the novel classes to generate sufficient node features so that any traditional supervised classifiers can be used to classify the query set. As a result, the proposed method is the first work of data augmentation for FSNC. Experiment results show the effectiveness and the efficiency of our proposed method, compared to state-of-the-art methods, in terms of different classification tasks.

Authors

• Zongqian Wu University of Electronic Science and Technology of China
• Peng Zhou Anhui University
• Guoqiu Wen Guangxi Key Lab of Multisource Information Mining Security, Guangxi Normal University
• Yingying Wan
• Junbo Ma School of Communication Engineering, Hangzhou Dianzi University, Hangzhou 310018, China Lishui Institute of Hangzhou Dianzi University, Hangzhou Dianzi University, Hangzhou 310018, China
• Debo Cheng University of South Australia, Australia
• Xiaofeng Zhu Guangxi Key Lab of Multisource Information Mining Security, Guangxi Normal University School of Computer Science and Engineering, University of Electronic Science and Technology of China

Keywords

• Machine Learning: Classification
• Machine Learning: Convolutional Networks
• Machine Learning: Few-shot learning
• Machine Learning: Semi-Supervised Learning