Self-Augmentation Graph Contrastive Learning for Multi-view Attribute Graph Clustering-Reference-Cited by-同舟云学术

Self-Augmentation Graph Contrastive Learning for Multi-view Attribute Graph Clustering

Published:2023-10-29 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 2023 Workshop on Advanced Multimedia Computing for Smart Manufacturing and Engineering
language:
Short-container-title:

Author:

Jin Shan¹^ORCID,Chen Zhikui¹^ORCID,Yu Shuo¹^ORCID,Altaf Muhammad¹^ORCID,Ma Zhenchao²^ORCID

Affiliation:

1. Dalian University of Technology, Dalian, China

2. University of British Columbia, Vancouver, BC, Canada

Funder

Fundamental Research Funds for the Central Universities

National Natural Science Foundation of China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3606042.3616455

Reference27 articles.

1. Multimedia data mining: state of the art and challenges

2. Jiafeng Cheng , Qianqian Wang , Zhiqiang Tao , Deyan Xie , and Quanxue Gao . 2021 . Multi-view attribute graph convolution networks for clustering . In Proceedings of the twenty-ninth international conference on international joint conferences on artificial intelligence. 2973--2979 . Jiafeng Cheng, Qianqian Wang, Zhiqiang Tao, Deyan Xie, and Quanxue Gao. 2021. Multi-view attribute graph convolution networks for clustering. In Proceedings of the twenty-ninth international conference on international joint conferences on artificial intelligence. 2973--2979.

3. Shaohua Fan , Xiao Wang , Chuan Shi , Emiao Lu , Ken Lin , and Bai Wang . 2020. One2Multi Graph Autoencoder for Multi-View Graph Clustering (WWW '20) . Association for Computing Machinery , New York, NY, USA , 3070--3076. Shaohua Fan, Xiao Wang, Chuan Shi, Emiao Lu, Ken Lin, and Bai Wang. 2020. One2Multi Graph Autoencoder for Multi-View Graph Clustering (WWW '20). Association for Computing Machinery, New York, NY, USA, 3070--3076.

4. Temporal Multimodal Learning in Audiovisual Speech Recognition

5. Joint Representation Learning and Novel Category Discovery on Single- and Multi-modal Data