Mingguo He

Mingguo He

National University of Singapore (NUS)

I am currently a Research Fellow at the School of Computing, National University of Singapore, working with Prof. Yatao Bian. Previously, I worked with Prof. Xiaokui Xiao at NUS. I obtained my Ph.D. degree in June 2025 from the Gaoling School of Artificial Intelligence, Renmin University of China, under the supervision of Prof. Zhewei Wei. Between October 2024 and May 2025, I was a visiting student at the Technical University of Munich, where I collaborated with Prof. Stephan Günnemann. Prior to my Ph.D., I earned a B.E. degree in Software Engineering from Central South University in June 2020.

My research interests lie at the intersection of Graph Machine Learning, Generative Modeling, and AI for Science (AI4Science), with a current focus on AI for Drug Discovery (AI4Drug). My previous research has primarily focused on Spectral Graph Neural Networks (GNNs) and Graph Transformers, leveraging spectral and structural properties to develop expressive and efficient graph learning methods. More recently, I have been exploring generative approaches, including diffusion models and energy-based learning, for molecular representation, generation, and optimization, aiming to accelerate drug discovery and molecular design.

🔥 News

  • 2026.04: Our paper, “Rethinking Link Prediction for Directed Graphs,” was accepted by IEEE TPAMI 2026.
  • 2026.01: Our paper, “Robustness in Text-Attributed Graph Learning: Insights, Trade-offs, and New Defenses,” was accepted at ICLR 2026.
  • 2025.05: We are excited to introduce a new benchmark for Directed Graph Link Prediction, DirLinkBench.
  • 2024.01: Our paper was accepted at TheWebConf 2024 and selected for oral presentation.

📝 Publications

  • Rethinking Link Prediction for Directed Graphs. [Paper][Code]
    Mingguo He, Yuhe Guo, Yanping Zheng, Zhewei Wei, Stephan Günnemann, Xiaokui Xiao
    Accepted by Transactions on Pattern Analysis and Machine Intelligence. (TPAMI 2026)

  • Robustness in Text-Attributed Graph Learning: Insights, Trade-offs, and New Defenses. [Paper][Code]
    Runlin Lei, Lu Yi, Mingguo He, Pengyu Qiu, Zhewei Wei, Yongchao Liu, Chuntao Hong
    In the Fourteenth International Conference on Learning Representations. (ICLR 2026)

  • PolyFormer: Scalable Node-wise Filters via Polynomial Graph Transformer. [Paper][Code]
    Jiahong Ma, Mingguo He, Zhewei Wei.
    In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. (KDD 2024)

  • Spectral Heterogeneous Graph Convolutions via Positive Noncommutative Polynomials. [Paper][Code]
    Mingguo He, Zhewei Wei, Shikun Feng, Zhengjie Huang, Weibin Li, Yu Sun, Dianhai Yu.
    In Proceedings of the ACM Web Conference 2024. (TheWebConf 2024, Oral 9.4%)

  • Convolutional Neural Networks on Graphs with Chebyshev Approximation, Revisited. [Paper][Code]
    Mingguo He, Zhewei Wei, Ji-Rong Wen.
    In the 36th Conference on Neural Information Processing Systems. (NeurIPS 2022, Oral 1.8%)

  • BernNet: Learning Arbitrary Graph Spectral Filters via Bernstein Approximation. [Paper][Code]
    Mingguo He, Zhewei Wei, Zengfeng Huang, Hongteng Xu.
    The 35th Conference on Neural Information Processing Systems. (NeurIPS 2021)

  • Approximate Graph Propagation. [Paper][Code]
    Hanzhi Wang, Mingguo He, Zhewei Wei, Sibo Wang, Ye Yuan, Xiaoyong Du, Ji-Rong Wen.
    In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. (KDD 2021)

🎖 Honors and Awards

  • 2024 National Scholarship for Doctoral Students
  • 2020 Provincial Excellent Graduates
  • 2018 National Scholarship for Undergraduate Students

📖 Educations

  • 2020.09 - 2025.06: Ph.D. in Artificial Intelligence from the Gaoling School of Artificial Intelligence, Renmin University of China.
  • 2024.10 - 2025.03: Visiting Researcher at the School of Computation, Information and Technology, Technical University of Munich.
  • 2016.09 - 2020.06: B.Eng. in Software Engineering, School of Computer Science, Central South University.

💼 Academic Services

I have served as an Area Chair, program committee member, and reviewer for the following conferences and journals:

Area Chair

  • International Conference on Learning Representations (ICLR 2027)

Program Committee Member / Reviewer

  • Conference on Neural Information Processing Systems (NeurIPS 2022–2025)
  • International Conference on Machine Learning (ICML 2023–2026)
  • International Conference on Learning Representations (ICLR 2024–2026)
  • ACM Web Conference (TheWebConf 2024–2026)
  • Learning on Graphs Conference (LoG 2024–2026)
  • AAAI Conference on Artificial Intelligence (AAAI 2025–2026)
  • ACM International Conference on Web Search and Data Mining (WSDM 2023–2025)

Journal Reviewer

  • IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)
  • IEEE Transactions on Knowledge and Data Engineering (TKDE)

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