Abstract
Citation networks, as an important type of graph-structured data, have been widely applied in fields such as academic research, scientific collaboration, and patent analysis. In this study, we construct a large-scale citation network dataset with rich citation relationships based on public datasets such as CNKI (China National Knowledge Infrastructure). We utilize Graph Convolutional Network (GCN) [1] for efficient classification and analysis in the domains of machine learning, deep learning, and neural networks. The experimental results demonstrate that our approach not only enhances the accuracy of citation network classification but also effectively captures complex relationships and local features among nodes, offering practicality and application value.
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Acknowledgement
This project is supported by Jilin Provincial Department of Education Science and Technology Project(JJKH20210457KJ,JJKH20221262KJ), the innovation project of The People's Republic of China ministry of education science and technology development center (No.2022IT096), Jilin province science and technology development program(20220508038RC),.
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Ran, L., Pei, Y., Dong, Y., Sun, H. (2024). Research on GCN Classification Model Based on CNKI Citation Network. In: Dong, J., Zhang, L., Cheng, D. (eds) Proceedings of the 2nd International Conference on Internet of Things, Communication and Intelligent Technology. IoTCIT 2023. Lecture Notes in Electrical Engineering, vol 1197. Springer, Singapore. https://doi.org/10.1007/978-981-97-2757-5_62
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DOI: https://doi.org/10.1007/978-981-97-2757-5_62
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