2 papers
cs.LG2024
SES: Bridging the Gap Between Explainability and Prediction of Graph Neural Networks
Zhenhua Huang, Kunhao Li, Shaojie Wang +3
Despite the Graph Neural Networks' (GNNs) proficiency in analyzing graph data, achieving high-accuracy and interpretable predictions remains challenging. Existing GNN interpreters…
cs.LG2024
Graph Structure Prompt Learning: A Novel Methodology to Improve Performance of Graph Neural Networks
Zhenhua Huang, Kunhao Li, Shaojie Wang +3
Graph neural networks (GNNs) are widely applied in graph data modeling. However, existing GNNs are often trained in a task-driven manner that fails to fully capture the intrinsic n…