Graph convolutional autoencoders with co-learning of graph structure and node attributes
•We propose a novel end-to-end graph autoencoders model for the attributed graph.•The proposed model can reconstruct both the graph structure and node attributes.•The graph encoder is a completely low-pass filter.•The graph decoder is a completely high-pass filter.•Show the effectiveness of the prop...
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| Vydáno v: | Pattern recognition Ročník 121; s. 108215 |
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Elsevier Ltd
01.01.2022
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| ISSN: | 0031-3203, 1873-5142 |
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| Abstract | •We propose a novel end-to-end graph autoencoders model for the attributed graph.•The proposed model can reconstruct both the graph structure and node attributes.•The graph encoder is a completely low-pass filter.•The graph decoder is a completely high-pass filter.•Show the effectiveness of the proposed model.
Recently, graph representation learning based on autoencoders has received much attention. However, these methods suffer from two limitations. First, most graph autoencoders ignore the reconstruction of either the graph structure or the node attributes, which often leads to a poor latent representation of the graph-structured data. Second, for existing graph autoencoders models, the encoder and decoder are mainly composed of an initial graph convolutional network (GCN) or its variants. These traditional GCN-based graph autoencoders more or less encounter the problem of incomplete filtering, which causes these models to be unstable in practical applications. To address the above issues, this paper proposes the Graph convolutional Autoencoders with co-learning of graph Structure and Node attributes (GASN) based on variational autoencoders. Specifically, the proposed GASN encodes and decodes the node attributes and graph structure comprehensively in the graph-structured data. Furthermore, we design a completely low-pass graph encoder and a high-pass graph decoder. The experimental results on real-world datasets demonstrate that the proposed GASN achieves state-of-the-art performance on node clustering, link prediction, and visualization tasks. |
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| AbstractList | •We propose a novel end-to-end graph autoencoders model for the attributed graph.•The proposed model can reconstruct both the graph structure and node attributes.•The graph encoder is a completely low-pass filter.•The graph decoder is a completely high-pass filter.•Show the effectiveness of the proposed model.
Recently, graph representation learning based on autoencoders has received much attention. However, these methods suffer from two limitations. First, most graph autoencoders ignore the reconstruction of either the graph structure or the node attributes, which often leads to a poor latent representation of the graph-structured data. Second, for existing graph autoencoders models, the encoder and decoder are mainly composed of an initial graph convolutional network (GCN) or its variants. These traditional GCN-based graph autoencoders more or less encounter the problem of incomplete filtering, which causes these models to be unstable in practical applications. To address the above issues, this paper proposes the Graph convolutional Autoencoders with co-learning of graph Structure and Node attributes (GASN) based on variational autoencoders. Specifically, the proposed GASN encodes and decodes the node attributes and graph structure comprehensively in the graph-structured data. Furthermore, we design a completely low-pass graph encoder and a high-pass graph decoder. The experimental results on real-world datasets demonstrate that the proposed GASN achieves state-of-the-art performance on node clustering, link prediction, and visualization tasks. |
| ArticleNumber | 108215 |
| Author | Liang, Jianqing Wang, Dianhui Liang, Jiye Yao, Kaixuan Wang, Jie |
| Author_xml | – sequence: 1 givenname: Jie surname: Wang fullname: Wang, Jie email: wangjie_reg@163.com organization: Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education, School of Computer and Information Technology, Shanxi University, Shanxi, Taiyuan 030006, China – sequence: 2 givenname: Jiye surname: Liang fullname: Liang, Jiye email: ljy@sxu.edu.cn organization: Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education, School of Computer and Information Technology, Shanxi University, Shanxi, Taiyuan 030006, China – sequence: 3 givenname: Kaixuan surname: Yao fullname: Yao, Kaixuan email: yaokx2@gmail.com organization: Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education, School of Computer and Information Technology, Shanxi University, Shanxi, Taiyuan 030006, China – sequence: 4 givenname: Jianqing surname: Liang fullname: Liang, Jianqing email: liangjq@sxu.edu.cn organization: Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education, School of Computer and Information Technology, Shanxi University, Shanxi, Taiyuan 030006, China – sequence: 5 givenname: Dianhui surname: Wang fullname: Wang, Dianhui email: dh.wang@latrobe.edu.au organization: Department of Computer Science and Information Technology, La Trobe University, Melbourne, VIC 3086, Australia |
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| Cites_doi | 10.1016/j.patcog.2017.03.014 10.1109/TKDE.2018.2807452 10.1109/TIP.2018.2877335 10.1145/3158369 10.1016/j.neunet.2020.04.028 10.1016/j.patcog.2017.10.013 10.1109/TIT.1982.1056489 10.1109/MSP.2012.2235192 |
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