87 citations · 122 across the 13 of their papers we have counts for
7 papers · 1 filter
GraphPatcher: Mitigating Degree Bias for Graph Neural Networks via Test-time Augmentation
Mingxuan Ju, Tong Zhao, Wenhao Yu +2
Recent studies have shown that graph neural networks (GNNs) exhibit strong biases towards the node degree: they usually perform satisfactorily on high-degree nodes with rich neighb…
Let Graph be the Go Board: Gradient-free Node Injection Attack for Graph Neural Networks via Reinforcement Learning
Mingxuan Ju, Yujie Fan, Chuxu Zhang +1
Graph Neural Networks (GNNs) have drawn significant attentions over the years and been broadly applied to essential applications requiring solid robustness or vigorous security sta…
Self-Supervised Graph Structure Refinement for Graph Neural Networks
Jianan Zhao, Qianlong Wen, Mingxuan Ju +2
Graph structure learning (GSL), which aims to learn the adjacency matrix for graph neural networks (GNNs), has shown great potential in boosting the performance of GNNs. Most exist…
Multi-task Self-supervised Graph Neural Networks Enable Stronger Task Generalization
Mingxuan Ju, Tong Zhao, Qianlong Wen +4
Self-supervised learning (SSL) for graph neural networks (GNNs) has attracted increasing attention from the graph machine learning community in recent years, owing to its capabilit…
Black-box Node Injection Attack for Graph Neural Networks
Mingxuan Ju, Yujie Fan, Yanfang Ye +1
Graph Neural Networks (GNNs) have drawn significant attentions over the years and been broadly applied to vital fields that require high security standard such as product recommend…
Adaptive Kernel Graph Neural Network
Mingxuan Ju, Shifu Hou, Yujie Fan +3
Graph neural networks (GNNs) have demonstrated great success in representation learning for graph-structured data. The layer-wise graph convolution in GNNs is shown to be powerful…
Heterogeneous Temporal Graph Neural Network
Yujie Fan, Mingxuan Ju, Chuxu Zhang +2
Graph neural networks (GNNs) have been broadly studied on dynamic graphs for their representation learning, majority of which focus on graphs with homogeneous structures in the spa…