{"type":"ScholarlyArticle","title":"Dual Self-Paced Graph Convolutional Network: Towards Reducing Attribute Distortions Induced by Topology","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":"Zhiyang Chen","slug":"zhiyang-chen","given":"Zhiyang","family":"Chen","affiliation":"School of Artificial Intelligence , Hebei University of Technology , China; Hebei Province Key Laboratory of Big Data Calculation , Hebei University of Technology , 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":"The success of graph convolutional neural networks (GCNNs) based semi-supervised node classification is credited to the attribute smoothing (propagating) over the topology. However, the attributes may be interfered by the utilization of the topology information. This distortion will induce a certain amount of misclassifications of the nodes, which can be correctly predicted with only the attributes. By analyzing the impact of the edges in attribute propagations, the simple edges, which connect two nodes with similar attributes, should be given priority during the training process compared to the complex ones according to curriculum learning. To reduce the distortions induced by the topology while exploit more potentials of the attribute information, Dual Self-Paced Graph Convolutional Network (DSP-GCN) is proposed in this paper. Specifically, the unlabelled nodes with confidently predicted labels are gradually added into the training set in the node-level self-paced learning, while edges are gradually, from the simple edges to the complex ones, added into the graph during the training process in the edge-level self-paced learning. These two learning strategies are designed to mutually reinforce each other by coupling the selections of the edges and unlabelled nodes. Experimental results of transductive semi-supervised node classification on many real networks indicate that the proposed DSP-GCN has successfully reduced the attribute distortions induced by the topology while it gives superior performances with only one graph convolutional layer.","doi":"10.24963/ijcai.2019/564","url":"https://ijcai.org/proceedings/2019/564","pdf":"https://ijcai.org/proceedings/2019/0564.pdf","markdown":"https://ijcai.org/proceedings/2019/564.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/564","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":564,"pages":{"first":4062,"last":4069},"keywords":["Machine Learning: Data Mining","Machine Learning Applications: Networks"],"videos":[],"references":[{"position":1,"raw":"[Belkin et al., 2006] Mikhail Belkin, Partha Niyogi, and Vikas Sindhwani. 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