37 citations · 80 across the 10 of their papers we have counts for
4 papers · 1 filter
Globally Interpretable Graph Learning via Distribution Matching
Yi Nian, Yurui Chang, Wei Jin +1
Graph neural networks (GNNs) have emerged as a powerful model to capture critical graph patterns. Instead of treating them as black boxes in an end-to-end fashion, attempts are ari…
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…
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…