3 citations · 4 across the 3 of their papers we have counts for
3 papers
cs.LG2022
Graph Contrastive Learning with Implicit Augmentations
Huidong Liang, Xingjian Du, Bilei Zhu +3
Existing graph contrastive learning methods rely on augmentation techniques based on random perturbations (e.g., randomly adding or dropping edges and nodes). Nevertheless, alterin…
cs.LG2021★ 1 cited
Wasserstein Adversarially Regularized Graph Autoencoder
Huidong Liang, Junbin Gao
This paper introduces Wasserstein Adversarially Regularized Graph Autoencoder (WARGA), an implicit generative algorithm that directly regularizes the latent distribution of node em…
cs.LG2021★ 3 cited
How Neural Processes Improve Graph Link Prediction
Huidong Liang, Junbin Gao
Link prediction is a fundamental problem in graph data analysis. While most of the literature focuses on transductive link prediction that requires all the graph nodes and majority…