📖 Selected Publications

Please kindly find my full publication list on Google Scholar.

Journals

• [Chem. Sci’23] On the Use of Real-World Datasets for Reaction Yield Prediction PDF
  • Mandana Saebi, Bozhao Nan, John Herr, Jessica Wahlers, Zhichun Guo, Andrzej Zurański, Thierry Kogej, Per-Ola Norrby, Abigail Doyle, Olaf Wiest, Nitesh V. Chawla
  • Chemical Science, IF:9.97, 2023
• [TVCG’22] SD$^2$: Slicing and Dicing Scholarly Data for Interactive Evaluation of Academic Performance PDF Code
  • Zhichun Guo, Jun Tao, Siming Chen, Nitesh V. Chawla, Chaoli Wang
  • IEEE Transactions on Visualization and Computer Graphics, IF:5.23, 2022

Conferences

• [NeurIPS’24] Pure Message Passing Can Estimate Common Neighbor for Link Prediction PDF
  • Kaiwen Dong, Zhichun Guo, Nitesh V Chawla
  • Conference on Neural Information Processing Systems, 2024
• [NeurIPS’24] Test-time Aggregation for Collaborative Filtering PDF
  • Mingxuan Ju, William Shiao, Zhichun Guo, Yanfang Ye, Yozen Liu, Neil Shah, Tong Zhao
  • Conference on Neural Information Processing Systems, 2024
• [NeurIPS’24] Can LLMs Solve Molecule Puzzles? A Multimodal Benchmark for Molecular Structure Elucidation
  • Kehan Guo, Bozhao Nan, Yujun Zhou, Taicheng Guo, Zhichun Guo, Mihir Surve, Zhenwen Liang, Nitesh V Chawla, Olaf Wiest, Xiangliang Zhang
  • Conference on Neural Information Processing Systems Datasets and Benchmarks Track, 2024
• [NeurIPS’23] What Indeed Can GPT Models Do in Chemistry? A Comprehensive Benchmark on Eight Tasks PDF
  • Taicheng Guo, Kehan Guo, Bozhao Nan, Zhenwen Liang, Zhichun Guo, Nitesh V. Chawla, Olaf Wiest, Xiangliang Zhang
  • Conference on Neural Information Processing Systems Datasets and Benchmarks Track, 2023
• [ICML’23] Linkless Link Prediction via Relational Distillation PDF Code
  • Zhichun Guo, William Shiao, Shichang Zhang, Yozen Liu, Nitesh Chawla, Neil Shah, Tong Zhao
  • International Conference on Machine Learning, 2023
• [IJCAI’23] Graph-based Molecular Representation Learning PDF
  • Zhichun Guo, Kehan Guo, Bozhao Nan, Yijun Tian, Roshni G. Iyer, Yihong Ma, Olaf Wiest, Xiangliang Zhang, Wei Wang, Chuxu Zhang, Nitesh V. Chawla
  • International Joint Conference on Artificial Intelligence Survey Track, 2023
• [ICLR’23] Link Prediction with Non-contrastive Learning PDF
  • William Shiao, Zhichun Guo, Tong Zhao, Evangelos E. Papalexakis, Yozen Liu, Neil Shah
  • International Conference on Learning Representations, 2023
• [ICLR’23] Learning MLPs on Graphs: A Unified View of Effectiveness, Robustness, and Efficiency PDF
  • Yijun Tian, Chuxu Zhang, Zhichun Guo, Xiangliang Zhang, Nitesh V. Chawla
  • International Conference on Learning Representations, 2023
• [AAAI’23] Boosting Graph Neural Networks via Adaptive Knowledge Distillation PDF
  • Zhichun Guo, Chunhui Zhang, Yujie Fan, Yijun Tian, Chuxu Zhang, Nitesh V. Chawla
  • AAAI Conference on Artificial Intelligence, 2023
• [LoG’22] FakeEdge: Alleviate Dataset Shift in Link Prediction PDF
  • Kaiwen Dong, Yijun Tian, Zhichun Guo, Yang Yang, Nitesh V. Chawla
  • Learning on Graphs Conference, 2022
• [LoG’22] Flashlight: Scalable Link Prediction with Effective Decoders PDF
  • Yiwei Wang, Bryan Hooi, Yozen Liu, Tong Zhao, Zhichun Guo, Neil Shah
  • Learning on Graphs Conference, 2022
• [WWW’21] Few-shot Graph Learning for Molecular Property Prediction PDF Code
  • Zhichun Guo, Chuxu Zhang, Wenhao Yu, John Herr, Olaf Wiest, Meng Jiang, Nitesh V. Chawla
  • The Web Conference, 2021
• [CIKM’20] GraSeq: Graph and Sequence Fusion Learning for Molecular Property Prediction PDF Code
  • Zhichun Guo, Wenhao Yu, Chuxu Zhang, Meng Jiang, Nitesh V. Chawla
  • ACM International Conference on Information and Knowledge Management, 2020