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Self-supervised robust Graph Neural Networks against noisy graphs and noisy labels

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Abstract

In the paper, we first explore a novel problem of training the robust Graph Neural Networks (GNNs) against noisy graphs and noisy labels. To the problem, we propose a general Self-supervised Robust Graph Neural Network framework that consists of three modules: graph structure learning, sample selection, and self-supervised learning. Specifically, we first employ a graph structure learning approach to obtain an optimal graph structure. Next, using this structure, we use a clustering algorithm to generate pseudo-labels that represent the clusters. We then design a sample selection strategy based on these pseudo-labels to select nodes with clean labels. Additionally, we introduce a self-supervised learning technique where low-level layer parameters are shared with GNNs to predict pseudo-labels. We jointly train the graph structure learning module, the GNNs model, and the self-supervised model. Finally, we conduct extensive experiments on four real-world datasets, demonstrating the superiority of our methods compared with state-of-the-art methods for semi-supervised node classification under noisy graphs and noisy labels.

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Authors and Affiliations

  1. Department of Computer Science and Technology, Nanjing University, No.163 Xianlin Avenue, Qixia District, Nanjing, 100190, China

    Jinliang Yuan, Hualei Yu, Meng Cao, Jianqing Song, Junyuan Xie & Chongjun Wang

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  1. Jinliang Yuan
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Correspondence to Jinliang Yuan or Chongjun Wang.

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This paper is supported by the National Natural Science Foundation of China (Grant No. 62192783, U1811462), the Collaborative Innovation Center of Novel Software Technology and Industrialization at Nanjing University. All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript.

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Yuan, J., Yu, H., Cao, M. et al. Self-supervised robust Graph Neural Networks against noisy graphs and noisy labels. Appl Intell 53, 25154–25170 (2023). https://doi.org/10.1007/s10489-023-04836-6

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