3 papers
cs.LG2026
Are LLM-Enhanced Graph Neural Networks Robust against Poisoning Attacks?
Yuhang Ma, Jie Wang, Zheng Yan
Large Language Models (LLMs) have advanced Graph Neural Networks (GNNs) by enriching node representations with semantic features, giving rise to LLM-enhanced GNNs that achieve nota…
cs.LG2025
GRExplainer: A Universal Explanation Method for Temporal Graph Neural Networks
Xuyan Li, Jie Wang, Zheng Yan
Dynamic graphs are widely used to represent evolving real-world networks. Temporal Graph Neural Networks (TGNNs) have emerged as a powerful tool for processing such graphs, but the…
cs.LG2025
CAT: Can Trust be Predicted with Context-Awareness in Dynamic Heterogeneous Networks?
Jie Wang, Zheng Yan, Jiahe Lan +2
Trust prediction provides valuable support for decision-making, risk mitigation, and system security enhancement. Recently, Graph Neural Networks (GNNs) have emerged as a promising…