Posts by Collection

MixupExplainer

Model Architecture for MixupExplainer

RegExplainer

Model Architecture for RegExplainer

Commit-level, Neural Vulnerability Detection and Assessment

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

DeMinify: Neural Variable Name Recovery and Type Inference

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

MixupExplainer: Generalizing Explanations for Graph Neural Networks with Data Augmentation

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

Interpreting Graph Neural Networks with In-Distributed Proxies

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

LLMExplainer: Large Language Model based Bayesian Inference for Graph Explanation Generation

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

RegExplainer: Generating Explanations for Graph Neural Networks in Regression Tasks

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

RISE: Radius of Influence based Subgraph Extraction for 3D Molecular Graph Explanation

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

GE-Chat: A Graph Enhanced RAG Framework for Evidential Response Generation of LLMs

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

Is Your Explanation Reliable: Confidence-Aware Explanation on Graph Neural Networks

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

RB-FT: Rationale-Bootstrapped Fine-Tuning for Video Classification

Published in arXiv preprint, 2025

A two-stage self-improvement framework that uses model-generated rationales to adapt vision-language models to domain-specific video classification without additional annotations.

Recommended citation: Meilong Xu, Di Fu, Jiaxing Zhang, Gong Yu, Jiayu Zheng, Xiaoling Hu, Dongdi Zhao, Feiyang Li, Chao Chen, Yong Cao. 2025. RB-FT: Rationale-Bootstrapped Fine-Tuning for Video Classification. arXiv:2511.15923. https://arxiv.org/abs/2511.15923

Shot-Aware Frame Sampling for Video Understanding

Published in arXiv preprint, 2026

InfoShot is a training-free, shot-aware sampler that preserves both representative context and brief decision-critical events in long videos.

Recommended citation: Mengyu Zhao, Di Fu, Yongyu Xie, Jiaxing Zhang, Zhigang Yuan, Shirin Jalali, Yong Cao. 2026. Shot-Aware Frame Sampling for Video Understanding. arXiv:2603.17374. https://arxiv.org/abs/2603.17374

Addressing Structural Distribution Shift in Explanations for Graph Neural Networks

Published in IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2026

A theoretical and empirical treatment of structural distribution shift in GNN explanations, using proxy graphs to preserve explanatory information while remaining in distribution.

Recommended citation: Zhuomin Chen, Hojat Allah Salehi, Esteban Schafir, Xu Zheng, Jiaxing Zhang, Hua Wei, Jingchao Ni, Farhad Shirani, Dongsheng Luo. 2026. Addressing Structural Distribution Shift in Explanations for Graph Neural Networks. IEEE Transactions on Pattern Analysis and Machine Intelligence. doi:10.1109/TPAMI.2026.3690304. https://doi.org/10.1109/TPAMI.2026.3690304

Address Out-Of-Distribution Problem in Explainable AI in Graph Neural Networks

Published: August 09, 2023

I did oral presentation for our paper “MixupExplainer: Generalizing Explanations for Graph Neural Networks with Data Augmentation” at KDD 2023. Download paper here

RegExplainer: Generating Explanations for Graph Neural Networks in Regression Task

Published: March 01, 2024

I did poster presentation for our paper “RegExplainer: Generating Explanations for Graph Neural Networks in Regression Task” at Learning on Graphs Conference 2024. Download paper here ```

IS218: Web Application Development

Undergraduate course, New Jersey Institute of Technology, Department of Information Systems, 2024

I taught a course on web application development using HTML, Python, Docker and Flask. I held office hours, graded assignments, and led lab sessions.