Published in KDD 2025, 2025
Confidence-calibrated framework for reliable GNN explanations, especially under OOD settings.
Recommended citation: Jiaxing Zhang, Xiaoou Liu, Dongsheng Luo, Hua Wei. 2025. Is Your Explanation Reliable: Confidence-Aware Explanation on Graph Neural Networks. Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2025). https://arxiv.org/abs/2506.00437
Published in IJCAI 2025 Demo Track, 2025
Graph-enhanced RAG pipeline for evidence-grounded LLM responses.
Recommended citation: Longchao Da, Parth Mitesh Shah, Kuan-Ru Liou, Jiaxing Zhang, Hua Wei. 2025. GE-Chat: A Graph Enhanced RAG Framework for Evidential Response Generation of LLMs. IJCAI 2025 Demo Track (arXiv:2505.10143). https://arxiv.org/abs/2505.10143
Published in ICML 2025, 2025
A 3D GNN explainer that localizes explanations through node-wise radius of influence.
Recommended citation: Jingxiang Qu, Wenhan Gao, Jiaxing Zhang, Xufeng Liu, Hua Wei, Haibin Ling, Yi Liu. 2025. RISE: Radius of Influence based Subgraph Extraction for 3D Molecular Graph Explanation. International Conference on Machine Learning (ICML 2025). https://arxiv.org/abs/2505.02247
Published in Advances in Neural Information Processing Systems (NeurIPS 2024), 2024
An explanation framework for graph regression models with improved reliability under distribution shift.
Recommended citation: Jiaxing Zhang, Zhuomin Chen, Hao Mei, Longchao Da, Dongsheng Luo, Hua Wei. 2024. RegExplainer: Generating Explanations for Graph Neural Networks in Regression Tasks. Advances in Neural Information Processing Systems 37 (NeurIPS 2024), 79282-79306. https://proceedings.neurips.cc/paper_files/paper/2024/hash/909f526db5127f8bd8158af32d9e313a-Abstract-Conference.html
Published in AIAgent4IR 2025 (in conjunction with KDD), 2024
Uses LLM-guided Bayesian inference to mitigate learning bias in graph explanation.
Recommended citation: Jiaxing Zhang, Jiayi Liu, Dongsheng Luo, Jennifer Neville, Hua Wei. 2025. LLMExplainer: Large Language Model based Bayesian Inference for Graph Explanation Generation. AIAgent4IR 2025 Workshop (in conjunction with KDD 2025). https://arxiv.org/abs/2407.15351
Published in ICML 2024, 2024
Generates in-distribution proxy graphs to improve faithfulness of GNN explanations.
Recommended citation: Zhuomin Chen, Jiaxing Zhang, Jingchao Ni, Xiaoting Li, Yuchen Bian, Md Mezbahul Islam, Ananda Mohan Mondal, Hua Wei, Dongsheng Luo. 2024. Interpreting Graph Neural Networks with In-Distributed Proxies. International Conference on Machine Learning (ICML 2024). https://arxiv.org/abs/2402.02036
Published in KDD 2023, 2023
A data-augmentation-based framework to improve OOD robustness of GNN explanations.
Recommended citation: Jiaxing Zhang, Dongsheng Luo, Hua Wei. 2023. MixupExplainer: Generalizing Explanations for Graph Neural Networks with Data Augmentation. Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2023). https://arxiv.org/abs/2307.07832
Published in ESEC/FSE 2023, 2023
Neural recovery of variable names and type inference from minified code.
Recommended citation: Yi Li, Aashish Yadavally, Jiaxing Zhang, Shaohua Wang, Tien N. Nguyen. 2023. DeMinify: Neural Variable Name Recovery and Type Inference. Proceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE 2023). https://dl.acm.org/doi/10.1145/3611643.3616232
Published in ESEC/FSE 2023, 2023
Commit-level vulnerability detection and CVSS assessment via context-aware graph learning.
Recommended citation: Yi Li, Aashish Yadavally, Jiaxing Zhang, Shaohua Wang, Tien N. Nguyen. 2023. Commit-level, Neural Vulnerability Detection and Assessment. Proceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE 2023). https://dl.acm.org/doi/10.1145/3611643.3616249