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Soft-GNN: towards robust graph neural networks via self-adaptive data utilization

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Abstract

Graph neural networks (GNNs) have gained traction and have been applied to various graph-based data analysis tasks due to their high performance. However, a major concern is their robustness, particularly when faced with graph data that has been deliberately or accidentally polluted with noise. This presents a challenge in learning robust GNNs under noisy conditions. To address this issue, we propose a novel framework called Soft-GNN, which mitigates the influence of label noise by adapting the data utilized in training. Our approach employs a dynamic data utilization strategy that estimates adaptive weights based on prediction deviation, local deviation, and global deviation. By better utilizing significant training samples and reducing the impact of label noise through dynamic data selection, GNNs are trained to be more robust. We evaluate the performance, robustness, generality, and complexity of our model on five real-world datasets, and our experimental results demonstrate the superiority of our approach over existing methods.

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Acknowledgment

The work was supported by the National Natural Science Foundation of China (Grant No. 62127808).

Author information

Authors and Affiliations

  1. National Engineering Research Center for Big Data Technology and System, Service Computing Technology and System Lab, Cluster and Grid Computing Lab, School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China

    Yao Wu, Hong Huang & Hai Jin

  2. College of Information and Communication, National University of Defense Technology, Wuhan, 430019, China

    Yao Wu

  3. Department of Computer Science and Operations Research, Université de Montréal, Montreal, H3C 3J7, Canada

    Yu Song

Authors
  1. Yao Wu
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  2. Hong Huang
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  3. Yu Song
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  4. Hai Jin
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Corresponding author

Correspondence to Hong Huang.

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Competing interests The authors declare that they have no competing interests or financial conflicts to disclose.

Additional information

Yao Wu is an assistant professor at the National University of Defense Technology, China. She obtained her PhD degree from Huazhong University of Science and Technology, China in 2023, following the completion of her BS degree from the same institution in 2016. Her primary research focus is on graph data mining and analysis.

Hong Huang is an associate professor at Huazhong University of Science and Technology, China. She received her PhD degree from the University of Göttingen, Germany in 2016 and her ME degree from Tsinghua University, China in 2012. Her research interests lie in social network analysis, data mining, and knowledge graph.

Yu Song received his BS degree in electronic information engineering and his ME degree in computer science, in 2018 and 2021, from Huazhong University of Science and Technology, China. Currently, he is pursuing his PhD degree at the Université de Montréal, Canada. His research interests include graph data mining and NLP.

Hai Jin is a Chair Professor of computer science and engineering at Huazhong University of Science and Technology (HUST) in China. Jin received his PhD degree in computer engineering from HUST in 1994. In 1996, he was awarded a German Academic Exchange Service fellowship to visit the Technical University of Chemnitz in Germany. Jin worked at The University of Hong Kong, China between 1998 and 2000, and as a visiting scholar at the University of Southern California, USA between 1999 and 2000. He was awarded Excellent Youth Award from the National Science Foundation of China in 2001. Jin is a Fellow of IEEE, Fellow of CCF, and a life member of the ACM. His research interests include computer architecture, parallel and distributed computing, big data processing, data storage, and system security.

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Wu, Y., Huang, H., Song, Y. et al. Soft-GNN: towards robust graph neural networks via self-adaptive data utilization. Front. Comput. Sci. 19, 194311 (2025). https://doi.org/10.1007/s11704-024-3575-5

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