Abstract
Graph convolutional networks (GCNs) have achieved great success in social networks and other aspects. However, existing GCN methods generally require a wealth of domain knowledge to obtain the data graph, which cannot guarantee that the graph is suitable. In this paper, we propose adaptive graph learning for semi-supervised classification of GCNs. Firstly, the hypergraph is used to establish the initial neighborhood relationship between data. Then hypergraph, sparse learning and adaptive graph are integrated into a framework. Finally, the suitable graph is obtained, which is inputted into GCN for semi-supervised learning. The experimental results of multi-type datasets show that our method is superior to other comparison algorithms in classification tasks.
This work is supported in part by the National Natural Science Foundation of China (Grant No: 81701780); the Guangxi Natural Science Foundation (Grant No: 2017GXNSFBA198221); the Project of Guangxi Science and Technology (GuiKeAD19110133, GuiKeAD20159041); the Innovation Project of Guangxi Graduate Education (Grants No: YCSW20201008, JXXYYJSCXXM-008); and the Hunan Provincial Science & Technology Project Foundation (2018TP1018, 2018RS3065).
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Wan, Y., Zhan, M., Li, Y. (2021). Adaptive Graph Learning for Semi-supervised Classification of GCNs. In: Qiao, M., Vossen, G., Wang, S., Li, L. (eds) Databases Theory and Applications. ADC 2021. Lecture Notes in Computer Science(), vol 12610. Springer, Cham. https://doi.org/10.1007/978-3-030-69377-0_2
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