2 citations · 3 across the 2 of their papers we have counts for
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cs.LG2024★ 1 cited
Training a Label-Noise-Resistant GNN with Reduced Complexity
Rui Zhao, Bin Shi, Zhiming Liang +3
Graph Neural Networks (GNNs) have been widely employed for semi-supervised node classification tasks on graphs. However, the performance of GNNs is significantly affected by label…
cs.LG2024
Graph Adversarial Diffusion Convolution
Songtao Liu, Jinghui Chen, Tianfan Fu +3
This paper introduces a min-max optimization formulation for the Graph Signal Denoising (GSD) problem. In this formulation, we first maximize the second term of GSD by introducing…
cs.LG2022★ 2 cited
How Powerful is Implicit Denoising in Graph Neural Networks
Songtao Liu, Rex Ying, Hanze Dong +3
Graph Neural Networks (GNNs), which aggregate features from neighbors, are widely used for graph-structured data processing due to their powerful representation learning capabiliti…