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Few-shot node classification via local adaptive discriminant structure learning

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Abstract

Node classification has a wide range of application scenarios such as citation analysis and social network analysis. In many real-world attributed networks, a large portion of classes only contain limited labeled nodes. Most of the existing node classification methods cannot be used for few-shot node classification. To train the model effectively and improve the robustness and reliability of the model with scarce labeled samples, in this paper, we propose a local adaptive discriminant structure learning (LADSL) method for few-shot node classification. LADSL aims to properly represent the nodes in the attributed graphs and learn a metric space with a strong discriminating power by reducing the intra-class variations and enlarging inter-class differences. Extensive experiments conducted on various attributed networks datasets demonstrate that LADSL is superior to the other methods on few-shot node classification task.

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Acknowledgements

This work was supported by the National Key R&D Program of China (2018YFB1402600), and the National Natural Science Foundation of China (Grant Nos. 61802028, 62192784, 61877006, and 62002027). We gratefully acknowledge the support of MindSpore, CANN (Compute Architecture for Neural Networks) and Ascend AI Processor used for this research.

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Authors and Affiliations

  1. Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia, School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, 100876, China

    Zhe Xue, Junping Du, Xin Xu, Xiangbin Liu, Junfu Wang & Feifei Kou

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  1. Zhe Xue
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  2. Junping Du
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  3. Xin Xu
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  4. Xiangbin Liu
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  5. Junfu Wang
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  6. Feifei Kou
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Corresponding author

Correspondence to Junping Du.

Additional information

Zhe Xue received the PhD degree in computer science from University of Chinese Academy of Sciences, China in 2017. He is currently an Associate Professor with the school of computer science, Beijing University of Posts and Telecommunications, China. His research interests include machine learning, data mining and multimedia data analysis.

Junping Du is now a Professor and PhD tutor at the School of Computer Science and Technology, Beijing University of Posts and Telecommunications, China. Her research interests include artificial intelligence, machine learning and pattern recognition.

Xin Xu is currently working toward the PhD degree with Beijing University of Posts and Telecommunications, China. Her research interests include machine learning, intelligent information processing and knowledge graph.

Xiangbin Liu received the BS degree in Computer Science and Technology from Jilin University, China in 2020. He is currently pursuing the MS degree in Intelligent information processing with the Beijing University of Posts and Telecommunications, China. His current research interest includes Computer vision, Multi-modal search.

Junfu Wang graduated from Beijing University of Posts and Telecommunications with a bachelor’s degree in computer Science and technology in 2020. He is currently studying for a master’s degree in computer science at Beijing University of Posts and Telecommunications, China. His research interest covers big data and intelligent information processing.

Feifei Kou received her PhD degree in School of Computer Science from Beijing University of Posts and Telecommunications, China in 2019. She ever did postdoctoral research in School of Computer Science from Beijing University of Posts and Telecommunications from 2019 to 2021. She is currently an lecturer in School of Computer Science (National Pilot software Engineering School), Beijing University of Posts and Telecommunications, China. Her research interests include semantic learning and multimedia information processing.

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Xue, Z., Du, J., Xu, X. et al. Few-shot node classification via local adaptive discriminant structure learning. Front. Comput. Sci. 17, 172316 (2023). https://doi.org/10.1007/s11704-022-1259-6

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