Deep embedded clustering with distribution consistency preservation for attributed networks
•A distribution consistency preserving deep embedded clustering model is proposed.•The model exploits GAE and AE to learn node representations and clusters jointly.•A consistency constraint is designed to maintain the consistency of the clusters.•The empirical study verifies the effectiveness of the...
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| Published in: | Pattern recognition Vol. 139; p. 109469 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier Ltd
01.07.2023
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| ISSN: | 0031-3203, 1873-5142 |
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| Abstract | •A distribution consistency preserving deep embedded clustering model is proposed.•The model exploits GAE and AE to learn node representations and clusters jointly.•A consistency constraint is designed to maintain the consistency of the clusters.•The empirical study verifies the effectiveness of the proposed model.
Many complex systems in the real world can be characterized as attributed networks. To mine the potential information in these networks, deep embedded clustering, which obtains node representations and clusters simultaneously, has been given much attention in recent years. Under the assumption of consistency for data in different views, the cluster structure of network topology and that of node attributes should be consistent for an attributed network. However, many existing methods ignore this property, even though they separately encode node representations from network topology and node attributes and cluster nodes on representation vectors learned from one of the views. Therefore, in this study, we propose an end-to-end deep embedded clustering model for attributed networks. It utilizes graph autoencoder and node attribute autoencoder to learn node representations and cluster assignments. In addition, a distribution consistency constraint is introduced to maintain the latent consistency of cluster distributions in two views. Extensive experiments on several datasets demonstrate that the proposed model achieves significantly better or competitive performance compared with the state-of-the-art methods. The source code can be found at https://github.com/Zhengymm/DCP. |
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| AbstractList | •A distribution consistency preserving deep embedded clustering model is proposed.•The model exploits GAE and AE to learn node representations and clusters jointly.•A consistency constraint is designed to maintain the consistency of the clusters.•The empirical study verifies the effectiveness of the proposed model.
Many complex systems in the real world can be characterized as attributed networks. To mine the potential information in these networks, deep embedded clustering, which obtains node representations and clusters simultaneously, has been given much attention in recent years. Under the assumption of consistency for data in different views, the cluster structure of network topology and that of node attributes should be consistent for an attributed network. However, many existing methods ignore this property, even though they separately encode node representations from network topology and node attributes and cluster nodes on representation vectors learned from one of the views. Therefore, in this study, we propose an end-to-end deep embedded clustering model for attributed networks. It utilizes graph autoencoder and node attribute autoencoder to learn node representations and cluster assignments. In addition, a distribution consistency constraint is introduced to maintain the latent consistency of cluster distributions in two views. Extensive experiments on several datasets demonstrate that the proposed model achieves significantly better or competitive performance compared with the state-of-the-art methods. The source code can be found at https://github.com/Zhengymm/DCP. |
| ArticleNumber | 109469 |
| Author | Jia, Caiyan Li, Xuanya Yu, Jian Zheng, Yimei |
| Author_xml | – sequence: 1 givenname: Yimei surname: Zheng fullname: Zheng, Yimei email: ymmzheng@bjtu.edu.cn organization: School of Computer and Information Technology, Beijing Jiaotong University, Beijing, 100044, China – sequence: 2 givenname: Caiyan surname: Jia fullname: Jia, Caiyan email: cyjia@bjtu.edu.cn organization: School of Computer and Information Technology, Beijing Jiaotong University, Beijing, 100044, China – sequence: 3 givenname: Jian surname: Yu fullname: Yu, Jian email: jianyu@bjtu.edu.cn organization: School of Computer and Information Technology, Beijing Jiaotong University, Beijing, 100044, China – sequence: 4 givenname: Xuanya surname: Li fullname: Li, Xuanya email: lixuanya@baidu.com organization: Baidu Online Network Technology (Beijing) Co., Ltd, Beijing, 100085, China |
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| Cites_doi | 10.1145/3385415 10.1016/j.patcog.2021.108230 10.1016/j.sigpro.2021.108310 10.1109/34.868688 10.1016/j.neucom.2020.04.120 10.3233/IDA-184121 10.1016/j.patcog.2021.108386 10.1016/j.patcog.2018.05.019 10.1109/TKDE.2018.2807452 10.1109/TBDATA.2018.2850013 10.1007/s41109-019-0237-x 10.1016/j.patcog.2021.108334 10.1016/j.patcog.2021.107996 10.1109/TSMC.2019.2897152 10.1109/TKDE.2018.2849727 10.1103/PhysRevE.78.046110 |
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| Keywords | Graph autoencoder Node representation learning Autoencoder Cluster distribution consistency Deep embedded clustering |
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