37 citations · 53 across the 4 of their papers we have counts for
4 papers
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…
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…
Graph Embedding with Hierarchical Attentive Membership
Lu Lin, Ethan Blaser, Hongning Wang
The exploitation of graph structures is the key to effectively learning representations of nodes that preserve useful information in graphs. A remarkable property of graph is that…
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…