Dual-decoder graph autoencoder for unsupervised graph representation learning
Unsupervised graph representation learning is a challenging task that embeds graph data into a low-dimensional space without label guidance. Recently, graph autoencoders have been proven to be an effective way to solve this problem in some attributed networks. However, most existing graph autoencode...
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| Veröffentlicht in: | Knowledge-based systems Jg. 234; S. 107564 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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Amsterdam
Elsevier B.V
25.12.2021
Elsevier Science Ltd |
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| ISSN: | 0950-7051, 1872-7409 |
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| Abstract | Unsupervised graph representation learning is a challenging task that embeds graph data into a low-dimensional space without label guidance. Recently, graph autoencoders have been proven to be an effective way to solve this problem in some attributed networks. However, most existing graph autoencoder-based embedding algorithms only reconstruct the feature maps of nodes or the affinity matrix but do not fully leverage the latent information encoded in the low-dimensional representation. In this study, we propose a dual-decoder graph autoencoder model for attributed graph embedding. The proposed framework embeds the graph topological structure and node attributes into a compact representation, and then the two decoders are trained to reconstruct the node attributes and graph structures simultaneously. The experimental results on clustering and link prediction tasks strongly support the conclusion that the proposed model outperforms the state-of-the-art approaches. |
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| AbstractList | Unsupervised graph representation learning is a challenging task that embeds graph data into a low-dimensional space without label guidance. Recently, graph autoencoders have been proven to be an effective way to solve this problem in some attributed networks. However, most existing graph autoencoder-based embedding algorithms only reconstruct the feature maps of nodes or the affinity matrix but do not fully leverage the latent information encoded in the low-dimensional representation. In this study, we propose a dual-decoder graph autoencoder model for attributed graph embedding. The proposed framework embeds the graph topological structure and node attributes into a compact representation, and then the two decoders are trained to reconstruct the node attributes and graph structures simultaneously. The experimental results on clustering and link prediction tasks strongly support the conclusion that the proposed model outperforms the state-of-the-art approaches. |
| ArticleNumber | 107564 |
| Author | Ding, Zhuanlian Zhang, Xingyi Li, Dashuang Sun, Dengdi Tang, Jin |
| Author_xml | – sequence: 1 givenname: Dengdi surname: Sun fullname: Sun, Dengdi email: sundengdi@163.com organization: Key Laboratory of Intelligent Computing & Signal Processing (ICSP), Ministry of Education, School of Artificial Intelligence, Anhui University, Hefei, 230601, China – sequence: 2 givenname: Dashuang surname: Li fullname: Li, Dashuang email: lidashuang96@163.com organization: Anhui Provincial Key Laboratory of Multimodal Cognitive Computing, School of Computer Science and Technology, Anhui University, Hefei, 230601, China – sequence: 3 givenname: Zhuanlian surname: Ding fullname: Ding, Zhuanlian email: dingzhuanlian@163.com organization: School of Internet, Anhui University, Hefei, 230039, China – sequence: 4 givenname: Xingyi orcidid: 0000-0002-5052-000X surname: Zhang fullname: Zhang, Xingyi email: xyzhanghust@gmail.com organization: Key Laboratory of Intelligent Computing & Signal Processing (ICSP), Ministry of Education, School of Artificial Intelligence, Anhui University, Hefei, 230601, China – sequence: 5 givenname: Jin surname: Tang fullname: Tang, Jin email: tangjin@ahu.edu.cn organization: Anhui Provincial Key Laboratory of Multimodal Cognitive Computing, School of Computer Science and Technology, Anhui University, Hefei, 230601, China |
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| Cites_doi | 10.1109/TKDE.2018.2807452 10.24963/ijcai.2020/411 10.1609/aaai.v34i04.5984 10.24963/ijcai.2018/362 10.1145/3097983.3098061 10.1109/TKDE.2016.2598561 10.1109/ICDM.2018.8626170 10.1145/2806416.2806512 10.1109/MSP.2012.2235192 10.1007/s10618-010-0210-x 10.1109/TKDE.2018.2849727 10.1109/ICCV.2019.00662 10.1609/aaai.v28i1.8950 10.1145/3132847.3132919 10.24963/ijcai.2019/509 10.1109/ICCV.2017.612 10.1609/aaai.v30i1.10179 10.1109/TIT.1982.1056489 10.1016/j.knosys.2018.03.022 10.1145/3132847.3132967 10.1145/3366423.3380079 10.1145/2736277.2741093 10.1002/nav.3800020109 10.1109/TKDE.2018.2819980 10.1109/TKDE.2017.2733530 10.1145/2939672.2939754 10.1145/3159652.3159706 10.1145/2939672.2939751 10.1109/TKDE.2015.2391115 10.1016/j.acha.2010.04.005 10.1145/2623330.2623726 10.1109/TKDE.2015.2492567 10.1145/3018661.3018667 |
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| Keywords | Graph clustering Graph representation learning Graph autoencoder Graph neural networks Graph embedding |
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