44 citations · 58 across the 3 of their papers we have counts for
3 papers
cs.LG2022★ 44 cited
G-Mixup: Graph Data Augmentation for Graph Classification
Xiaotian Han, Zhimeng Jiang, Ninghao Liu +1
This work develops \emph{mixup for graph data}. Mixup has shown superiority in improving the generalization and robustness of neural networks by interpolating features and labels b…
cs.LG2022
Geometric Graph Representation Learning via Maximizing Rate Reduction
Xiaotian Han, Zhimeng Jiang, Ninghao Liu +3
Learning discriminative node representations benefits various downstream tasks in graph analysis such as community detection and node classification. Existing graph representation…
cs.LG2022★ 14 cited
FMP: Toward Fair Graph Message Passing against Topology Bias
Zhimeng Jiang, Xiaotian Han, Chao Fan +4
Despite recent advances in achieving fair representations and predictions through regularization, adversarial debiasing, and contrastive learning in graph neural networks (GNNs), t…