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Graph convolutional networks of reconstructed graph structure with constrained Laplacian rank

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Abstract

Convolutional neural networks (CNNs) have achieved unprecedented competitiveness in text and two-dimensional image data processing because of its good accuracy performance and high detection speed. Graph convolutional networks (GCNs), as an extension of classical CNNs in graph data processing, have attracted wide attention. At present, GCNs often use domain knowledge (such as citation recommendation system, biological cell networks) or artificial created fixed graph to achieve various semi-supervised classication tasks. Poor quality graph will lead to suboptimal results of semi-supervised classification tasks. We propose a more general GCN of reconstructed graph structure with constrained Laplacian rank. First, we use hypergraph to establish multivariate relationships between data. On the basis of the hypergraph, In virtue of Laplacian rank constraint to the graph matrix, we learn a new graph structure which has c connected components (where c is the number of classification), and then we construct an ideal graph matrix which is more suitable for the task of semi-supervised classification on GCNs. Finally, the data and the new graph are input GCNs model to get the results of classification. Experiments on 10 different datasets demonstrate that this method is more competitive than the comparison method.

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Notes

  1. http://archive.ics.uci.edu/ml/.

  2. https://blog.csdn.net/qq_32892383/article/details/104424358

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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. College of Computer Science and Information Technology, Guangxi Normal University, Guilin, Guangxi, 541004, People’s Republic of China

    Mengmeng Zhan, Guangquan Lu & Yingying Wan

  2. School of Natural and Computational Sciences, Massey University Auckland Campus, Auckland, 0745, New Zealand

    Jiangzhang Gan

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  1. Mengmeng Zhan
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  2. Jiangzhang Gan
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  3. Guangquan Lu
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  4. Yingying Wan
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Correspondence to Guangquan Lu.

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Zhan, M., Gan, J., Lu, G. et al. Graph convolutional networks of reconstructed graph structure with constrained Laplacian rank. Multimed Tools Appl 81, 34183–34194 (2022). https://doi.org/10.1007/s11042-020-09984-2

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