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Semi-Supervised Classification of Graph Convolutional Networks with Laplacian Rank Constraints

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Abstract

Graph convolutional networks (GCNs), as an extension of classic convolutional neural networks (CNNs) in graph processing, have achieved good results in completing semi-supervised learning tasks. Traditional GCNs usually use fixed graph to complete various semi-supervised classification tasks, such as chemical molecules and social networks. Graph is an important basis for the classification of GCNs model, and its quality has a large impact on the performance of the model. For low-quality input graph, the classification results of the GCNs model are often not ideal. In order to improve the classification effect of GCNs model, we propose a graph learning method to generate high-quality topological graph, which is more suitable for GCNs model classification. We use the correlation between the data to generate a data similarity matrix, and apply Laplacian rank constraint to similarity matrix, so that the number of connected components of the topological graph is consistent with the number of categories of the original data. Experimental results on 10 real datasets show that our method is better than the comparison method in classification effect.

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Acknowledgements

This work is partially supported by the Key Program of the National Natural Science Foundation of China (Grant No: 61836016); the Natural Science Foundation of China (Grants No: 61876046, 81701780, 61672177 and 61972177); the Project of Guangxi Science and Technology (GuiKeAD17195062); the Guangxi Natural Science Foundation (Grant No: 2017GXNSFBA198221); the Guangxi Collaborative Innovation Center of Multi-Source Information Integration and Intelligent Processing; the Research Fund of Guangxi Key Lab of Multisource Information Mining & Security (18-A-01-01); 2019 basic scientific research capability enhancement project for middle-aged teachers in guangxi university (2019KY0062); and Innovation Project of Guangxi Graduate Education (Grants No:YCSW20201008, JXXYYJSCXXM-008).

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Authors and Affiliations

  1. Guangxi Key Lab of Multi-Source Information Mining and Security, Guangxi Normal University, Guilin, Guangxi, China

    Haiqi Zhang, Guangquan Lu, Mengmeng Zhan & Beixian Zhang

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  1. Haiqi Zhang
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  2. Guangquan Lu
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  4. Beixian Zhang
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Correspondence to Guangquan Lu.

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Zhang, H., Lu, G., Zhan, M. et al. Semi-Supervised Classification of Graph Convolutional Networks with Laplacian Rank Constraints. Neural Process Lett 54, 2645–2656 (2022). https://doi.org/10.1007/s11063-020-10404-7

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