most citedRevisiting Edge Perturbation for Graph Neural Network in Graph Data Augmentation and Attack

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cs.LG2024★ 1 cited

Revisiting Edge Perturbation for Graph Neural Network in Graph Data Augmentation and Attack

Xin Liu, Yuxiang Zhang, Meng Wu +6

Edge perturbation is a basic method to modify graph structures. It can be categorized into two veins based on their effects on the performance of graph neural networks (GNNs), i.e.…

cs.LG2023

Towards Complex Dynamic Physics System Simulation with Graph Neural ODEs

Guangsi Shi, Daokun Zhang, Ming Jin +2

The great learning ability of deep learning models facilitates us to comprehend the real physical world, making learning to simulate complicated particle systems a promising endeav…