{"type":"ScholarlyArticle","title":"Attributed Graph Clustering: A Deep Attentional Embedding Approach","authors":[{"name":"Chun Wang","slug":"chun-wang","given":"Chun","family":"Wang","affiliation":"Centre for Artificial Intelligence , University of Technology Sydney , Australia"},{"name":"Shirui Pan","slug":"shirui-pan","given":"Shirui","family":"Pan","affiliation":"Faculty of IT , Monash University , Australia"},{"name":"Ruiqi Hu","slug":"ruiqi-hu","given":"Ruiqi","family":"Hu","affiliation":"Centre for Artificial Intelligence , University of Technology Sydney , Australia"},{"name":"Guodong Long","slug":"guodong-long","given":"Guodong","family":"Long","affiliation":"Centre for Artificial Intelligence , University of Technology Sydney , Australia"},{"name":"Jing Jiang","slug":"jing-jiang","given":"Jing","family":"Jiang","affiliation":"Centre for Artificial Intelligence , University of Technology Sydney , Australia"},{"name":"Chengqi Zhang","slug":"chengqi-zhang","given":"Chengqi","family":"Zhang","affiliation":"Centre for Artificial Intelligence , University of Technology Sydney , Australia"}],"abstract":"Graph clustering is a fundamental task which discovers communities or groups in networks. Recent studies have mostly focused on developing deep learning approaches to learn a compact graph embedding, upon which classic clustering methods like k-means or spectral clustering algorithms are applied. These two-step frameworks are difficult to manipulate and usually lead to suboptimal performance, mainly because the graph embedding is not goal-directed, i.e., designed for the specific clustering task. In this paper, we propose a goal-directed deep learning approach, Deep Attentional Embedded Graph Clustering (DAEGC for short). Our method focuses on attributed graphs to sufficiently explore the two sides of information in graphs. By employing an attention network to capture the importance of the neighboring nodes to a target node, our DAEGC algorithm encodes the topological structure and node content in a graph to a compact representation, on which an inner product decoder is trained to reconstruct the graph structure. Furthermore, soft labels from the graph embedding itself are generated to supervise a self-training graph clustering process, which iteratively refines the clustering results. The self-training process is jointly learned and optimized with the graph embedding in a unified framework, to mutually benefit both components. Experimental results compared with state-of-the-art algorithms demonstrate the superiority of our method.","doi":"10.24963/ijcai.2019/509","url":"https://ijcai.org/proceedings/2019/509","pdf":"https://ijcai.org/proceedings/2019/0509.pdf","markdown":"https://ijcai.org/proceedings/2019/509.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/509","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"Machine Learning M-Z","track":"Main track","number":509,"pages":{"first":3670,"last":3676},"keywords":["Machine Learning: Unsupervised Learning","Machine Learning: Clustering"],"videos":[],"references":[{"position":1,"raw":"[Cao et al., 2016] Shaosheng Cao, Wei Lu, and Qiongkai Xu. 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