• Corpus ID: 246863555

G-Mixup: Graph Data Augmentation for Graph Classification

@article{Han2022GMixupGD,
  title={G-Mixup: Graph Data Augmentation for Graph Classification},
  author={Xiaotian Han and Zhimeng Jiang and Ninghao Liu and Xia Hu},
  journal={ArXiv},
  year={2022},
  volume={abs/2202.07179}
}
This work develops mixup for graph data . Mixup has shown superiority in improving the generalization and robustness of neural networks by interpolating features and labels between two random samples. Traditionally, Mixup can work on regular, grid-like, and Euclidean data such as image or tabular data. However, it is challenging to directly adopt Mixup to augment graph data because different graphs typically: 1) have different numbers of nodes; 2) are not readily aligned; and 3) have unique… 

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