37 citations · 53 across the 4 of their papers we have counts for
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cs.LG2022★ 16 cited
Spectral Augmentation for Self-Supervised Learning on Graphs
Lu Lin, Jinghui Chen, Hongning Wang
Graph contrastive learning (GCL), as an emerging self-supervised learning technique on graphs, aims to learn representations via instance discrimination. Its performance heavily re…
cs.LG2021
Graph Structural Attack by Perturbing Spectral Distance
Lu Lin, Ethan Blaser, Hongning Wang
Graph Convolutional Networks (GCNs) have fueled a surge of research interest due to their encouraging performance on graph learning tasks, but they are also shown vulnerability to…
cs.LG2021★ 37 cited
Unbiased Graph Embedding with Biased Graph Observations
Nan Wang, Lu Lin, Jundong Li +1
Graph embedding techniques are pivotal in real-world machine learning tasks that operate on graph-structured data, such as social recommendation and protein structure modeling. Emb…