Robust graph learning with graph convolutional network |
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| Institution: | 1. School of Management Engineering, Shandong Jianzhu University, Jinan, China;2. School of Business, University of Shanghai for Science and Technology, Shanghai, China;1. Business School, Hohai University, Nanjing, China;2. Faculty of Education, The University of Hong Kong, Hong Kong, China;3. School of Information Science, The University of Texas at Austin, TX, USA;1. Cryptography and Cognitive Informatics Laboratory, AGH University of Science and Technology, 30 Mickiewicza Ave, Krakow 30-059, Poland;2. School of Computing, Engineering and Mathematical Sciences, La Trobe University, Melbourne, Australia;3. Department of Computer Science, Ryerson University, Canada |
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| Abstract: | Graph convolutional network (GCN) is a powerful tool to process the graph data and has achieved satisfactory performance in the task of node classification. In general, GCN uses a fixed graph to guide the graph convolutional operation. However, the fixed graph from the original feature space may contain noises or outliers, which may degrade the effectiveness of GCN. To address this issue, in this paper, we propose a robust graph learning convolutional network (RGLCN). Specifically, we design a robust graph learning model based on the sparse constraint and strong connectivity constraint to achieve the smoothness of the graph learning. In addition, we introduce graph learning model into GCN to explore the representative information, aiming to learning a high-quality graph for the downstream task. Experiments on citation network datasets show that the proposed RGLCN outperforms the existing comparison methods with respect to the task of node classification. |
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| Keywords: | Graph convolutional network Node classification Sparse constraint |
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