Abstract
The graph convolutional network has achieved great success since its proposal. Since GCN can be used to study non-Euclidean data, it extends convolutional networks for real-world applications. Graph data is a prevalent data structure in the real world and is widely used in various fields. Nowadays, most GCN models take data as a complete structure for input. However, real-world data is often incomplete for various reasons, and some data is missing features. Therefore, we propose a GCN model for completing missing data (PGCN) based on the coupled P systems. It can express the missing features of the data using the Gaussian mixture model and attention mechanism. In addition, based on the input, a new activation function is computed in the first layer of the GCN. The proposed PGCN method performs the node classification task on three datasets, and the results show that the method’s performance is better than existing missing data processing methods.
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Acknowledgment
This activity was financially supported in part by the National Natural Science Foundation of China. The National Natural Science Foundation of China (Nos. 621722622, 61876101,61802234 and 61806114), the Social Science Foundation of Shandong Province (16BGLJ06, 11CGLJ22), China Postdoctoral Science Foundation Project (2017M612339, 2018M642695). Natural Science Foundation of Shandong Province (ZR2019QF007), China Postdoctoral Special Funding Program (2019T120607) and the Youth Fund for Humanities and Social Sciences of the Ministry of Education. Youth Fund for Humanities and Social Sciences, Ministry of Education (19YJCZH244).
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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Chi, R., Liu, X. (2023). Improvement of Graph Convolution Network of Missing Data Based on P Systems. In: Huang, DS., Premaratne, P., Jin, B., Qu, B., Jo, KH., Hussain, A. (eds) Advanced Intelligent Computing Technology and Applications. ICIC 2023. Lecture Notes in Computer Science(), vol 14089. Springer, Singapore. https://doi.org/10.1007/978-981-99-4752-2_25
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DOI: https://doi.org/10.1007/978-981-99-4752-2_25
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