3 citations · 3 across the 5 of their papers we have counts for
5 papers
Prioritized Propagation in Graph Neural Networks
Yao Cheng, Minjie Chen, Xiang Li +2
Graph neural networks (GNNs) have recently received significant attention. Learning node-wise message propagation in GNNs aims to set personalized propagation steps for different n…
DropMix: Better Graph Contrastive Learning with Harder Negative Samples
Yueqi Ma, Minjie Chen, Xiang Li
While generating better negative samples for contrastive learning has been widely studied in the areas of CV and NLP, very few work has focused on graph-structured data. Recently,…
Graph Self-Contrast Representation Learning
Minjie Chen, Yao Cheng, Ye Wang +2
Graph contrastive learning (GCL) has recently emerged as a promising approach for graph representation learning. Some existing methods adopt the 1-vs-K scheme to construct one posi…
MUSE: Multi-View Contrastive Learning for Heterophilic Graphs
Mengyi Yuan, Minjie Chen, Xiang Li
In recent years, self-supervised learning has emerged as a promising approach in addressing the issues of label dependency and poor generalization performance in traditional GNNs.…
A Novel Noise Injection-based Training Scheme for Better Model Robustness
Zeliang Zhang, Jinyang Jiang, Minjie Chen +3
Noise injection-based method has been shown to be able to improve the robustness of artificial neural networks in previous work. In this work, we propose a novel noise injection-ba…