{"type":"ScholarlyArticle","title":"Topology Optimization based Graph Convolutional Network","authors":[{"name":"Liang Yang","slug":"liang-yang","given":"Liang","family":"Yang","affiliation":"School of Artificial Intelligence , Hebei University of Technology , China; State Key Laboratory of Information Security , Institute of Information Engineering , CAS , China; Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education , China"},{"name":"Zesheng Kang","slug":"zesheng-kang","given":"Zesheng","family":"Kang","affiliation":"School of Artificial Intelligence , Hebei University of Technology , China"},{"name":"Xiaochun Cao","slug":"xiaochun-cao","given":"Xiaochun","family":"Cao","affiliation":"State Key Laboratory of Information Security , Institute of Information Engineering , CAS , China"},{"name":"Di Jin","slug":"di-jin","given":"Di","family":"Jin","affiliation":"College of Intelligence and Computing , Tianjin University , China"},{"name":"Bo Yang","slug":"bo-yang","given":"Bo","family":"Yang","affiliation":"College of Computer Science and Technology , Jilin University , China; Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education , China"},{"name":"Yuanfang Guo","slug":"yuanfang-guo","given":"Yuanfang","family":"Guo","affiliation":"School of Computer Science and Engineering , Beihang University , China"}],"abstract":"In the past few years, semi-supervised node classification in attributed network has been developed rapidly. Inspired by the success of deep learning, researchers adopt the convolutional neural network to develop the Graph Convolutional Networks (GCN), and they have achieved surprising classification accuracy by considering the topological information and employing the fully connected network (FCN). However, the given network topology may also induce a performance degradation if it is directly employed in classification, because it may possess high sparsity and certain noises. Besides, the lack of learnable filters in GCN also limits the performance. In this paper, we propose a novel Topology Optimization based Graph Convolutional Networks (TO-GCN) to fully utilize the potential information by jointly refining the network topology and learning the parameters of the FCN. According to our derivations, TO-GCN is more flexible than GCN, in which the filters are fixed and only the classifier can be updated during the learning process. Extensive experiments on real attributed networks demonstrate the superiority of the proposed TO-GCN against the state-of-the-art approaches.","doi":"10.24963/ijcai.2019/563","url":"https://ijcai.org/proceedings/2019/563","pdf":"https://ijcai.org/proceedings/2019/0563.pdf","markdown":"https://ijcai.org/proceedings/2019/563.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/563","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":563,"pages":{"first":4054,"last":4061},"keywords":["Machine Learning: Data Mining","Machine Learning Applications: Networks"],"videos":[],"references":[{"position":1,"raw":"[Bahdanau et al., 2015] Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 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