Publications

Preprints

1. LLM and GNN are Complementary: Distilling LLM for Multimodal Graph Learning
   Junjie Xu, Zongyu Wu, Minhua Lin, Xiang Zhang, Suhang Wang
   [Paper]

2. Robustness-Inspired Defense Against Backdoor Attacks on Graph Neural Network
   Zhiwei Zhang, Minhua Lin, Junjie Xu, Zongyu Wu, Enyan Dai, Suhang Wang
   [Paper]

3. Self-Explainable Graph Neural Networks for Link Prediction
   Huaisheng Zhu, Dongsheng Luo, Xianfeng Tang, Junjie Xu, Hui Liu, Suhang Wang
   [Paper]

4. A Comprehensive Survey of Small Language Models in the Era of Large Language Models: Techniques, Enhancements, Applications, Collaboration with LLMs, and Trustworthiness
   Fali Wang, Zhiwei Zhang, Xianren Zhang, Zongyu Wu, Tzuhao Mo, Qiuhao Lu, Wanjing Wang, Rui Li, Junjie Xu, Xianfeng Tang, Qi He, Yao Ma, Ming Huang, Suhang Wang
   [Paper]

Conference Paper

1. Beyond Sequence: Impact of Geometric Context for RNA Property Prediction
   Junjie Xu, Artem Moskalev, Tommaso Mansi, Mangal Prakash, Rui Liao
   (AIDrugX @ NeurIPS 2024) NeurIPS 2024 Workshop on AI for New Drug Modalities [Paper]

2. Stealing Training Graphs from Graph Neural Networks
   Minhua Lin, Enyan Dai, Junjie Xu, Jinyuan Jia, Xiang Zhang, Suhang Wang
   (KDD 2025) 31th SIGKDD Conference on Knowledge Discovery and Data Mining

3. Shape-aware Graph Spectral Learning
   Junjie Xu, Enyan Dai, Dongsheng Luo, Xiang Zhang, Suhang Wang
   (CIKM 2024) ACM International Conference on Information and Knowledge Management [Paper] [Code]

4. Enhancing GNNs with Limited Labeled Data by Actively Distilling Knowledge from LLMs
   Quan Li, Tianxiang Zhao, Lingwei Chen, Junjie Xu, and Suhang Wang
   (BigData 2024) 2024 IEEE International Conference on Big Data [Paper]

5. HC-GST: Heterophily-aware Distribution Consistency-based Graph Self-training
   Fali Wang, Tianxiang Zhao, Junjie Xu, Suhang Wang
   (CIKM 2024) ACM International Conference on Information and Knowledge Management [Paper]

6. HP-GMN: Graph Memory Networks for Heterophilous Graphs
   Junjie Xu, Enyan Dai, Xiang Zhang, Suhang Wang
   (ICDM 2022) The IEEE International Conference on Data Mining [Paper] [Code]

7. Revisiting Time Series Outlier Detection: Definitions and Benchmarks
   Kwei-Herng Lai, Daochen Zha, Junjie Xu, Yue Zhao, Guanchu Wang, Xia Hu
   (NeurIPS 2021) Neural Information Processing Systems, Datasets and Benchmarks Track [Paper] [Code]

8. TODS: An Automated Time Series Outlier Detection System
   Kwei-Herng Lai, Daochen Zha, Guanchu Wang, Junjie Xu, Yue Zhao, Devesh Kumar, Yile Chen, Purav Zumkhawaka, Mingyang Wan, Diego Martinez, Xia Hu
   (AAAI 2021) AAAI Conference on Artificial Intelligence, demo track [Paper] [Code]

Journal Paper

1. A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainability
   Enyan Dai, Tianxiang Zhao, Huaisheng Zhu, Junjie Xu, Zhimeng Guo, Hui Liu, Jiliang Tang, Suhang Wang
   Machine Intelligence Research [Paper]