{"type":"ScholarlyArticle","title":"Masked Graph Convolutional Network","authors":[{"name":"Liang Yang","slug":"liang-yang","given":"Liang","family":"Yang","affiliation":"School of Artificial Intelligence , Hebei University of Technology , China; Hebei Province Key Laboratory of Big Data Calculation , Hebei University of Technology , China"},{"name":"Fan Wu","slug":"fan-wu","given":"Fan","family":"Wu","affiliation":"School of Artificial Intelligence , Hebei University of Technology , China; Hebei Province Key Laboratory of Big Data Calculation , Hebei University of Technology , China"},{"name":"Yingkui Wang","slug":"yingkui-wang","given":"Yingkui","family":"Wang","affiliation":"College of Intelligence and Computing , Tianjin University , China"},{"name":"Junhua Gu","slug":"junhua-gu","given":"Junhua","family":"Gu","affiliation":"School of Artificial Intelligence , Hebei University of Technology , China; Hebei Province Key Laboratory of Big Data Calculation , Hebei University of Technology , China"},{"name":"Yuanfang Guo","slug":"yuanfang-guo","given":"Yuanfang","family":"Guo","affiliation":"School of Computer Science and Engineering , Beihang University , China"}],"abstract":"Semi-supervised classification is a fundamental technology to  process the structured and unstructured data  in machine learning field. The traditional  attribute-graph based semi-supervised classification methods propagate  labels over the graph  which is usually constructed from the data features, while the graph convolutional neural networks smooth the node attributes, i.e., propagate the attributes,  over the real graph topology. In this paper, they are interpreted from the  perspective of propagation, and accordingly categorized into symmetric and asymmetric propagation based methods. From the perspective of propagation, both the traditional and network based methods are propagating certain objects over the graph. However, different from the label propagation,  the intuition ``the connected data samples tend to be similar in terms of the attributes\", in attribute propagation is only partially valid. Therefore, a masked graph convolution network (Masked GCN) is proposed by only propagating a certain portion of the attributes to the neighbours  according to a masking indicator, which is learned for each node  by jointly considering the attribute distributions in local neighbourhoods and the impact on the classification results. Extensive experiments on transductive and inductive node classification tasks have demonstrated the superiority of the proposed method.","doi":"10.24963/ijcai.2019/565","url":"https://ijcai.org/proceedings/2019/565","pdf":"https://ijcai.org/proceedings/2019/0565.pdf","markdown":"https://ijcai.org/proceedings/2019/565.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/565","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":565,"pages":{"first":4070,"last":4077},"keywords":["Machine Learning Applications: Networks","Machine Learning: Data Mining"],"videos":[],"references":[{"position":1,"raw":"[Bahdanau et al., 2015] Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 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