82 citations · 85 across the 4 of their papers we have counts for
7 papers
QuanGCN: Noise-Adaptive Training for Robust Quantum Graph Convolutional Networks
Kaixiong Zhou, Zhenyu Zhang, Shengyuan Chen +4
Quantum neural networks (QNNs), an interdisciplinary field of quantum computing and machine learning, have attracted tremendous research interests due to the specific quantum advan…
Graph Contrastive Learning with Personalized Augmentation
Xin Zhang, Qiaoyu Tan, Xiao Huang +1
Graph contrastive learning (GCL) has emerged as an effective tool for learning unsupervised representations of graphs. The key idea is to maximize the agreement between two augment…
Towards Deeper Graph Neural Networks with Differentiable Group Normalization
Kaixiong Zhou, Xiao Huang, Yuening Li +3
Graph neural networks (GNNs), which learn the representation of a node by aggregating its neighbors, have become an effective computational tool in downstream applications. Over-sm…
Auto-GNN: Neural Architecture Search of Graph Neural Networks
Kaixiong Zhou, Qingquan Song, Xiao Huang +1
Graph neural networks (GNN) has been successfully applied to operate on the graph-structured data. Given a specific scenario, rich human expertise and tremendous laborious trials a…
SpecAE: Spectral AutoEncoder for Anomaly Detection in Attributed Networks
Yuening Li, Xiao Huang, Jundong Li +2
Anomaly detection aims to distinguish observations that are rare and different from the majority. While most existing algorithms assume that instances are i.i.d., in many practical…
Multi-Label Adversarial Perturbations
Qingquan Song, Haifeng Jin, Xiao Huang +1
Adversarial examples are delicately perturbed inputs, which aim to mislead machine learning models towards incorrect outputs. While most of the existing work focuses on generating…