4 papers · 1 filter
Tensor-Fused Multi-View Graph Contrastive Learning
Yujia Wu, Junyi Mo, Elynn Chen +1
Graph contrastive learning (GCL) has emerged as a promising approach to enhance graph neural networks' (GNNs) ability to learn rich representations from unlabeled graph-structured…
Bridging Domain Adaptation and Graph Neural Networks: A Tensor-Based Framework for Effective Label Propagation
Tao Wen, Elynn Chen, Yuzhou Chen +1
Graph Neural Networks (GNNs) have recently become the predominant tools for studying graph data. Despite state-of-the-art performance on graph classification tasks, GNNs are overwh…
Conditional Uncertainty Quantification for Tensorized Topological Neural Networks
Yujia Wu, Bo Yang, Yang Zhao +3
Graph Neural Networks (GNNs) have become the de facto standard for analyzing graph-structured data, leveraging message-passing techniques to capture both structural and node featur…
Conditional Prediction ROC Bands for Graph Classification
Yujia Wu, Bo Yang, Elynn Chen +2
Graph classification in medical imaging and drug discovery requires accuracy and robust uncertainty quantification. To address this need, we introduce Conditional Prediction ROC (C…