{"type":"ScholarlyArticle","title":"Multi-View Attribute Graph Convolution Networks for Clustering","authors":[{"name":"Jiafeng Cheng","slug":"jiafeng-cheng","given":"Jiafeng","family":"Cheng","affiliation":"State Key Laboratory of Integrated Services Networks , Xidian University"},{"name":"Qianqian Wang","slug":"qianqian-wang","given":"Qianqian","family":"Wang","affiliation":"State Key Laboratory of Integrated Services Networks , Xidian University"},{"name":"Zhiqiang Tao","slug":"zhiqiang-tao","given":"Zhiqiang","family":"Tao","affiliation":"Northeastern University"},{"name":"Deyan Xie","slug":"deyan-xie","given":"Deyan","family":"Xie","affiliation":"State Key Laboratory of Integrated Services Networks , Xidian University"},{"name":"Quanxue Gao","slug":"quanxue-gao","given":"Quanxue","family":"Gao","affiliation":"State Key Laboratory of Integrated Services Networks , Xidian University; Unmanned System Research Institue , Northwestern Polytechnical university"}],"abstract":"Graph neural networks (GNNs) have made considerable achievements in processing graph-structured data. However, existing methods can not allocate learnable weights to different nodes in the neighborhood and lack of robustness on account of neglecting both node attributes and graph reconstruction. Moreover, most of multi-view GNNs mainly focus on the case of multiple graphs, while designing GNNs for solving graph-structured data of multi-view attributes is still under-explored. In this paper, we propose a novel Multi-View Attribute Graph Convolution Networks (MAGCN) model for the clustering task. MAGCN is designed with two-pathway encoders that map graph embedding features and learn the view-consistency information. Specifically, the first pathway develops multi-view attribute graph attention networks to reduce the noise/redundancy and learn the graph embedding features for each multi-view graph data. The second pathway develops consistent embedding encoders to capture the geometric relationship and probability distribution consistency among different views, which adaptively finds a consistent clustering embedding space for multi-view attributes. Experiments on three benchmark graph datasets show the superiority of our method compared with several state-of-the-art algorithms.","doi":"10.24963/ijcai.2020/411","url":"https://ijcai.org/proceedings/2020/411","pdf":"https://ijcai.org/proceedings/2020/0411.pdf","markdown":"https://ijcai.org/proceedings/2020/411.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/411","volume":{"slug":"2020","title":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2020","url":"https://ijcai.org/proceedings/2020/","year":2020,"location":"Yokohama","dates":"January 2021 (postponed from 11-17 July 2020)","editors":"Christian Bessiere","isbn":"978-0-9992411-6-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2020"},"section":"Machine Learning","track":"Main track","number":411,"pages":{"first":2973,"last":2979},"keywords":["Machine Learning: Unsupervised Learning","Machine Learning: Clustering","Machine Learning: Deep Learning","Machine Learning: Multi-instance;Multi-label;Multi-view learning"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/24044/file_24044.m3u8","length_sec":286,"provider":"hls"}],"references":[{"position":1,"raw":"[Andrew et al., 2013] Galen Andrew, Raman Arora, Jeff Bilmes, and Karen Livescu. 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