research-article

GRAE: Graph Recurrent Autoencoder for Multi-view Graph Clustering

Authors:

Jia ZhuAuthors Info & Claims

ACAI '21: Proceedings of the 2021 4th International Conference on Algorithms, Computing and Artificial Intelligence

Article No.: 72, Pages 1 - 9

https://doi.org/10.1145/3508546.3508618

Published: 25 February 2022 Publication History

Abstract

Multi-view graph clustering aims to discover communities or groups in the graph with multiple views, which usually can supply more comprehensive information than that single-view graph clustering. With the increasing scale of complex data from the real world, multi-view graph clustering has drawn much attention. It has a solid theoretical foundation and high effectiveness in applications such as data mining and social network analysis. However, Most existing methods obtain the clustering result only through the shared feature representations, defectively overlooking the unique features of multiple views. To fill this gap, a Graph Recurrent AutoEncoder (GRAE) is proposed for attributed multi-view graph clustering, which can attain node representation well by learning different view features. Specifically, we first design a global graph autoencoder and a partial graph autoencoder to extract the shared features and the unique features of all views, respectively, which can better represent the nodes in the graph. Then, from the perspective of representation fusion, we adopt an adaptive weight learning method to fuse the different features according to the importance of features. Moreover, we investigate a self-training clustering method to optimize a clustering objective for improving the clustering effect. Finally, we conducted a large number of experiments on three real-world datasets, demonstrating the superior performance of our proposed GRAE model on the multi-view graph clustering task.

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Cited By

Qin YZhang XYu SFeng G(2025)A survey on representation learning for multi-view dataNeural Networks10.1016/j.neunet.2024.106842181(106842)Online publication date: Jan-2025
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https://doi.org/10.3390/electronics13091788
Shabani NWu JBeheshti ASheng QFoo JHaghighi VHanif AShahabikargar M(2024)A Comprehensive Survey on Graph Summarization With Graph Neural NetworksIEEE Transactions on Artificial Intelligence10.1109/TAI.2024.33505455:8(3780-3800)Online publication date: Aug-2024
https://doi.org/10.1109/TAI.2024.3350545
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ACAI '21: Proceedings of the 2021 4th International Conference on Algorithms, Computing and Artificial Intelligence

December 2021

699 pages

ISBN:9781450385053

DOI:10.1145/3508546

Copyright © 2021 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 25 February 2022

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ACAI'21: 2021 4th International Conference on Algorithms, Computing and Artificial Intelligence

December 22 - 24, 2021

Sanya, China

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Cited By

Qin YZhang XYu SFeng G(2025)A survey on representation learning for multi-view dataNeural Networks10.1016/j.neunet.2024.106842181(106842)Online publication date: Jan-2025
https://doi.org/10.1016/j.neunet.2024.106842
Zhao XHou ZWang J(2024)Local-Global Representation Enhancement for Multi-View Graph ClusteringElectronics10.3390/electronics1309178813:9(1788)Online publication date: 6-May-2024
https://doi.org/10.3390/electronics13091788
Shabani NWu JBeheshti ASheng QFoo JHaghighi VHanif AShahabikargar M(2024)A Comprehensive Survey on Graph Summarization With Graph Neural NetworksIEEE Transactions on Artificial Intelligence10.1109/TAI.2024.33505455:8(3780-3800)Online publication date: Aug-2024
https://doi.org/10.1109/TAI.2024.3350545
Bai RHuang RChen YQin YXu YZheng Q(2024)A hierarchical consensus learning model for deep multi-view document clusteringInformation Fusion10.1016/j.inffus.2024.102507111(102507)Online publication date: Nov-2024
https://doi.org/10.1016/j.inffus.2024.102507
Chen MLin JLi XLiu BWang CHuang DLai J(2022)Representation Learning in Multi-view Clustering: A Literature ReviewData Science and Engineering10.1007/s41019-022-00190-87:3(225-241)Online publication date: 1-Aug-2022
https://doi.org/10.1007/s41019-022-00190-8

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