Selected Publications

See a selection of Shirui’s 10 publications below. Check out for more information about TrustAGI Lab.

Group 1: Top Journal Papers

  1. Koh, H.Y., Nguyen, A.T.N.#, Pan, S.#, May, L.T.#, Webb, G.I.#, 2024. Physicochemical graph neural network for learning protein–ligand interaction fingerprints from sequence data. Nature Machine Intelligence. [IF: 18.8; Featured on Phys.org, The Medical News, and Australian Manufacturing Magazine]
  2. Zhang, M., Li, H., Pan, S.#, Chang, X., Zhou, C., Ge, Z., Su, S.W., 2021. One-Shot Neural Architecture Search: Maximising Diversity to Overcome Catastrophic Forgetting. IEEE Transactions on Pattern Analysis and Machine Intelligence, TPAMI 43, 2921–2935. [CORE A*; IF: 20.8]
  3. Pan S, Luo L, Wang Y, Chen C, Wang J, Wu X, Unifying Large Language Models and Knowledge Graphs: A Roadmap. IEEE Trans Knowl Data Eng. (TKDE). 2024 [CORE A*; IF:8.9; 550+ Citations]

Group 2: Award Winning Papers

  1. Wu, Z., Pan, S.#, Chen, F., Long, G., Zhang, C., Yu, P.S., 2020. A comprehensive survey on graph neural networks. IEEE Transactions on Neural Networks and Learning Systems (TNNLS) 32, 4–24. [IEEE CIS TNNLS Outstanding Paper Award; 10,000+ Citations; JCR Q1]
  2. Wu, M., Pan, S., & Zhu, X. OpenWGL: Open-World Graph Learning. In IEEE International Conference on Data Mining, ICDM, November 17-20, 2020, Sorrento, Italy, 2020 [IEEE ICDM Best Student Paper Award; CORE A*]

Group 3: Highly Cited Papers

  1. Wu, Z., Pan, S.#, Long, G., Jiang, J., Zhang, C., 2019. Graph WaveNet for Deep Spatial-Temporal Graph Modeling, in: International Joint Conference on Artificial Intelligence (IJCAI), IJCAI-19. [CORE A*; 2,100+ Citations; #1 most cited IJCAI 2019 paper by June 2024]
  2. Wu, Z., Pan, S.#, Long, G., Jiang, J., Chang, X., Zhang, C., 2020. Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks, in: ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD-20. [CORE A*; 1,400+ Citations, #1 most cited KDD 2020 paper by June 2024]
  3. Pan, S.#, Hu, R., Long, G., Jiang, J., Yao, L., Zhang, C., 2018. Adversarially Regularized Graph Autoencoder for Graph Embedding, in: International Joint Conference on Artificial Intelligence, IJCAI-18. pp. 2609–2615. [CORE A*; 1,000+ Citations]

Group 4: Recent Top Conference Papers

  1. Jin, M., Li, Y.-F., Pan, S.#, 2022. Neural Temporal Walks: Motif-Aware Representation Learning on Continuous-Time Dynamic Graphs, in: Advances in Neural Information Processing Systems (NeurIPS). [CORE A*; 70+ Citations]
  2. Luo, L., Li, Y.-F., Haffari, G., Pan, S.#, 2024. Reasoning on graphs: Faithful and interpretable large language model reasoning, in: International Conference on Learning Representations (ICLR). [CORE A*; 100+ Citations]

# indicates corresponding author.