{"type":"ScholarlyArticle","title":"Multi-view Unsupervised Graph Representation Learning","authors":[{"name":"Jiangzhang Gan","slug":"jiangzhang-gan","given":"Jiangzhang","family":"Gan","affiliation":"Center for Future Media and School of Computer Science and Technology , University of Electronic Science and Technology of China , Chengdu 611731 , China; School of Mathematical and Computational Science , Massey University Auckland Campus , Auckland 0632 , New Zealand"},{"name":"Rongyao Hu","slug":"rongyao-hu","given":"Rongyao","family":"Hu","affiliation":"School of Mathematical and Computational Science , Massey University Auckland Campus , Auckland 0632 , New Zealand"},{"name":"Mengmeng Zhan","slug":"mengmeng-zhan","given":"Mengmeng","family":"Zhan","affiliation":"Center for Future Media and School of Computer Science and Technology , University of Electronic Science and Technology of China , Chengdu 611731 , China"},{"name":"Yujie Mo","slug":"yujie-mo","given":"Yujie","family":"Mo","affiliation":"Center for Future Media and School of Computer Science and Technology , University of Electronic Science and Technology of China , Chengdu 611731 , China"},{"name":"Yingying Wan","slug":"yingying-wan","given":"Yingying","family":"Wan","affiliation":"Center for Future Media and School of Computer Science and Technology , University of Electronic Science and Technology of China , Chengdu 611731 , China"},{"name":"Xiaofeng Zhu","slug":"xiaofeng-zhu","given":"Xiaofeng","family":"Zhu","affiliation":"Center for Future Media and School of Computer Science and Technology , University of Electronic Science and Technology of China , Chengdu 611731 , China; Shenzhen Institute for Advanced Study , University of Electronic Science and Technology of China , Shenzhen 518000 , China"}],"abstract":"Both data augmentation and contrastive loss are the key components of contrastive learning. In this paper, we design a new multi-view unsupervised graph representation learning method including adaptive data augmentation and multi-view contrastive learning, to address some issues of contrastive learning ignoring the information from feature space. Specifically, the adaptive data augmentation  first builds a feature graph from the feature space, and then designs a deep graph learning model on the original representation and the topology graph to update the feature graph and the new representation. As a result, the adaptive data augmentation outputs multi-view information, which is fed into two GCNs to generate multi-view embedding features. Two kinds of contrastive losses are further designed on multi-view embedding features to explore the complementary information among the topology and feature graphs. Additionally, adaptive data augmentation and contrastive learning are embedded in a unified framework to form an end-to-end model. Experimental results verify the effectiveness of our proposed method, compared to  state-of-the-art methods.","doi":"10.24963/ijcai.2022/414","url":"https://ijcai.org/proceedings/2022/414","pdf":"https://ijcai.org/proceedings/2022/0414.pdf","markdown":"https://ijcai.org/proceedings/2022/414.md","bibtex":"https://ijcai.org/proceedings/2022/bibtex/414","volume":{"slug":"2022","title":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2022","url":"https://ijcai.org/proceedings/2022/","year":2022,"location":"Vienna","dates":"23-29 July 2022","editors":"Luc De Raedt","isbn":"978-1-956792-00-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2022"},"section":"Machine Learning","track":"Main Track","number":414,"pages":{"first":2987,"last":2993},"keywords":["Machine Learning: Unsupervised Learning","Machine Learning: Multi-view learning"],"videos":[{"label":"Video #1","url":"https://slideslive.com/38986828","length_sec":70,"provider":"slideslive"},{"label":"Video #2","url":"https://slideslive.com/38984971","length_sec":322,"provider":"slideslive"}],"references":[{"position":1,"raw":"[Chen et al., 2019] Yu Chen, Lingfei Wu, and Mohammed J Zaki. 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