Explaining Graph Neural Networks with Large Language Models: A Counterfactual Perspective on Molecule Graphs. - Yinhan He; Zaiyi Zheng; Patrick Soga; Yaochen Zhu; Yushun Dong; Jundong Li (EMNLP (Findings), 2024)

Causal Inference with Latent Variables: Recent Advances and Future Prospectives. - Yaochen Zhu; Yinhan He; Jing Ma 002; Mengxuan Hu; Sheng Li 001; Jundong Li (KDD, 2024)

Safety in Graph Machine Learning: Threats and Safeguards. - Song Wang 013; Yushun Dong; Binchi Zhang; Zihan Chen 002; Xingbo Fu; Yinhan He; Cong Shen 001; Chuxu Zhang; Nitesh V. Chawla; Jundong Li (CoRR, 2024)

Spectral Greedy Coresets for Graph Neural Networks. - Mucong Ding; Yinhan He; Jundong Li; Furong Huang (CoRR, 2024)

Explaining Graph Neural Networks with Large Language Models: A Counterfactual Perspective for Molecular Property Prediction. - Yinhan He; Zaiyi Zheng; Patrick Soga; Yaozhen Zhu; Yushun Dong; Jundong Li (CoRR, 2024)

Virtual Nodes Can Help: Tackling Distribution Shifts in Federated Graph Learning. - Xingbo Fu; Zihan Chen 002; Yinhan He; Song Wang 013; Binchi Zhang; Chen Chen 022; Jundong Li (AAAI, 2025)

Graph Neural Networks Are More Than Filters: Revisiting and Benchmarking from A Spectral Perspective. - Yushun Dong; Patrick Soga; Yinhan He; Song Wang 013; Jundong Li (ICLR, 2025)

Edge Prompt Tuning for Graph Neural Networks. - Xingbo Fu; Yinhan He; Jundong Li (ICLR, 2025)

Demystify Epidemic Containment in Directed Networks: Theory and Algorithms. - Yinhan He; Chen Chen 022; Song Wang 013; Guanghui Min; Jundong Li (WSDM, 2025)

Global Graph Counterfactual Explanation: A Subgraph Mapping Approach. - Yinhan He; Wendy Zheng; Yaochen Zhu; Jing Ma 002; Saumitra Mishra; Natraj Raman; Ninghao Liu; Jundong Li (Trans. Mach. Learn. Res., 2025)

Energy-Based Models for Predicting Mutational Effects on Proteins. - Patrick Soga; Zhenyu Lei 004; Yinhan He; Camille L. Bilodeau; Jundong Li (CoRR, 2025)