Jieming SHI

Dr. Jieming SHI

Assistant Professor
Department of Computing
The Hong Kong Polytechnic University

I am looking for highly self-motivated PhD students, PostDocs, and research assistants. If you are interested, please send me your CV. Thanks! (All CVs are carefully evaluated. Only matched candidates will be responded.)

Research Interests

Big data analytics, databases, machine learning -- especially large-scale graph algorithms, graph learning, GPU computing, and heterogeneous data management.

Email:

Short Bio

I am an assistant professor at the Department of Computing, The Hong Kong Polytechnic University (PolyU). I obtained a PhD in Computer Science from The University of Hong Kong (HKU), advised by Prof. Nikos Mamoulis and Prof. David W. Cheung, and did a postdoc at School of Computing, National University of Singapore (NUS), advised by Prof. Xiaokui XIAO. I obtained bachelor degree from Nanjing University.

Selected Honors

  • 2022 ACM SIGMOD Research Highlight Award
  • Best Research Paper Award in VLDB 2021

Selected Publications (Full list, Google scholar, DBLP)
[ # Corresponding Author, * Co-first]

  1. A Versatile Framework for Attributed Network Clustering via K-Nearest Neighbor Augmentation [Paper] (To appear)
    Yiran Li, Gongyao Guo, Jieming Shi#, Renchi Yang, Shiqi Shen, Qing Li, Jun Luo.
    VLDBJ 2024, in the VLDB Journal, 2024.

  2. SGOOD: Substructure-enhanced Graph-Level Out-of-Distribution Detection [Paper] (To appear)
    Zhihao Ding, Jieming Shi#, Shiqi Shen, Xuequn Shang, Jiannong Cao, Zhipeng Wang, Zhi Gong.
    CIKM 2024, in the Proceedings of the ACM International Conference on Information and Knowledge Management, 2024.

  3. Effective Illicit Account Detection on Large Cryptocurrency MultiGraphs [Paper] (To appear)
    Zhihao Ding, Jieming Shi#, Qing Li and Jiannong Cao.
    CIKM 2024, in the Proceedings of the ACM International Conference on Information and Knowledge Management, 2024.

  4. Effective Clustering on Large Attributed Bipartite Graphs [Paper] (To appear)
    Renchi Yang, Yidu Wu, Xiaoyang Lin, Qichen Wang, Tsz Nam Chan, Jieming Shi.
    KDD 2024, in the Proceedings of the ACM SIGKDD international conference on Knowledge discovery and data mining, 2024.

  5. Efficient High-Quality Clustering for Large Bipartite Graphs [Paper]
    Renchi Yang, Jieming Shi#.
    SIGMOD 2024, in the Proceedings of the ACM Conference on Management of Data, 2024.

  6. Minimum Strongly Connected Subgraph Collection in Dynamic Graphs [Paper]
    Xin Chen, Jieming Shi#, You Peng, Wenqing Lin, Sibo Wang, Wenjie Zhang.
    PVLDB 2024, in the Proceedings of the VLDB Endowment, 2024.

  7. BOURNE: Bootstrapped Self-supervised Learning Framework for Unified Graph Anomaly Detection [Paper]
    Jie Liu, Mengting He, Xuequn Shang, Jieming Shi, Bin Cui, Hongzhi Yin
    ICDE 2024, in the Proceedings of the IEEE International Conference on Data Engineering, 2024.

  8. Low Latency and Sparse Computing Spiking Neural Networks With Self-Driven Adaptive Threshold Plasticity. [Paper]
    Anguo Zhang, Jieming Shi, Junyi Wu, Yongcheng Zhou, Wei Yu.
    TNNLS 2023, IEEE Transactions on Neural Networks and Learning Systems, 2023.

  9. LD2: Scalable Heterophilous Graph Neural Network with Decoupled Embeddings. [Paper]
    Ningyi Liao, Siqiang Luo, Xiang Li, Jieming Shi.
    NeurIPS 2023, in the Proceedings of the Advances in Neural Information Processing Systems, 2023.

  10. Effective and Efficient Route Planning Using Historical Trajectories on Road Networks [Paper] [Code]
    Tian Wei, Jieming Shi, Siqiang Luo, Hui Li, Xike Xie, Yuanhang Zou.
    PVLDB 2023, in the Proceedings of the VLDB Endowment, 2023.

  11. EmbedX: A Versatile, Efficient and Scalable Platform to Embed Both Graphs and High-Dimensional Sparse Data [Paper] [Code]
    Yuanhang Zou*, Zhihao Ding*, Jieming Shi, Shuting Guo, Chunchen Su, Yafei Zhang.
    PVLDB 2023, in the Proceedings of the VLDB Endowment, 2023.

  12. SlotGAT: Slot-based Message Passing for Heterogeneous Graphs (Heterogeneous Graph Neural Networks). [Paper] [Code]
    Ziang Zhou, Jieming Shi, Renchi Yang, Yuanhang Zou, Qing Li
    ICML 2023, International Conference on Machine Learning, 2023.

  13. Efficient and Effective Attributed Hypergraph Clustering via K-Nearest Neighbor Augmentation. [Paper] [Code]
    Yiran Li, Renchi Yang, Jieming Shi
    SIGMOD 2023, in the Proceedings of the ACM Conference on Management of Data, 2023.

  14. PANE: Scalable and Effective Attributed Network Embedding. [Paper] [Code]
    Renchi Yang, Jieming Shi, Xiaokui Xiao, Yin Yang, Sourav S. Bhowmick, Juncheng Liu
    VLDB Journal, 2023.

  15. Effective Stabilized Self-Training on Few-Labeled Graph Data. [Paper]
    Ziang Zhou, Jieming Shi, Shengzhong Zhang, Zengfeng Huang, Qing Li
    Information Sciences (JCR Q1), 2023.

  16. No PANE, No Gain: Scaling Attributed Network Embedding in a Single Server. (2022 ACM SIGMOD Research Highlight Award) [Paper]
    Renchi Yang, Jieming Shi, Xiaokui Xiao, Yin Yang, Sourav S Bhowmick, Juncheng Liu
    ACM SIGMOD Record, 2022.

  17. Scalable and Effective Bipartite Network Embedding. [Paper]
    Renchi Yang, Jieming Shi, Keke Huang, Xiaokui Xiao
    SIGMOD 2022, in the Proceedings of the ACM Conference on Management of Data, 2022.

  18. Example-based Spatial Search at Scale. [Paper]
    Hanyuan Zhang, Siqiang Luo, Jieming Shi, Jing Nathan Yan, Weiwei Sun
    ICDE 2022, in IEEE International Conference on Data Engineering, 2022.

  19. MOTS: Minimax Optimal Thompson Sampling. [Paper]
    Tianyuan Jin, Pan Xu, Jieming Shi, Xiaokui Xiao, and Quanquan Gu
    ICML 2021, in the Proceedings of the International Conference on Machine Learning, 2021.

  20. Unconstrained Submodular Maximization with Modular Costs: Tight Approximation and Application to Profit Maximization. [Paper]
    Tianyuan Jin, Yu Yang, Renchi Yang, Jieming Shi#, Keke Huang, and Xiaokui Xiao.
    PVLDB 2021, in the Proceedings of the VLDB Endowment, 14(10):1756-768, 2021.

  21. Effective and Scalable Clustering on Massive Attributed Graphs. [Paper]
    Renchi Yang, Jieming Shi#, Yin Yang, Keke Huang, Shiqi Zhang and Xiaokui Xiao
    TheWebConf (WWW) 2021, in the Proceedings of The Web Conference, 2021.

  22. Scaling Attributed Network Embedding to Massive Graphs. (Best Research Paper Award in VLDB 2021) [Paper]
    Renchi Yang, Jieming Shi, Xiaokui Xiao, Yin Yang, Juncheng Liu, Sourav S Bhowmick.
    PVLDB 2020, in the Proceedings of the VLDB Endowment, 2020.

  23. Multi-task Learning for Recommendation over Heterogeneous Information Network.
    Hui Li, Yanlin Wang, Ziyu Lyu, Jieming Shi.
    TKDE 2020, IEEE Transactions on Knowledge and Data Engineering, 2020.

  24. Realtime Index-Free Single Source SimRank Processing on Web-Scale Graphs.
    Jieming Shi*, Tianyuan Jin*, Renchi Yang, Xiaokui Xiao, Yin Yang. [Code]
    PVLDB 2020, in the Proceedings of the VLDB Endowment, 2020.

  25. Homogeneous Network Embedding for Massive Graphs via Reweighted Personalized PageRank.
    Renchi Yang, Jieming Shi, Xiaokui Xiao, Yin Yang, Sourav S Bhowmick.
    PVLDB 2020, in the Proceedings of the VLDB Endowment, 2020.

  26. Realtime Top-k Personalized PageRank over Large Graphs on GPUs. [Technical Report]
    Jieming Shi, Renchi Yang, Tianyuan Jin, Xiaokui Xiao, Yin Yang. [Code]
    PVLDB 2020, in the Proceedings of the VLDB Endowment, 2020.

  27. Efficient Pure Exploration in Adaptive Round model.
    Tianyuan Jin, Jieming Shi, Xiaokui Xiao, Enhong Chen. [Code]
    NeurIPS 2019, in the Proceedings of the Advances in Neural Information Processing Systems, 2019.

  28. Top-k Relevant Semantic Place Retrieval on Spatio-temporal RDF Data.
    Dingming Wu, Hao Zhou, Jieming Shi#, Nikos Mamoulis.
    VLDBJ 2019, International Journal on Very Large Data Bases, 2019.

  29. Density-based Place Clustering Using Geo-Social Network Data.
    Dingming Wu, Jieming Shi#, Nikos Mamoulis.
    TKDE 2018, IEEE Transactions on Knowledge and Data Engineering, 2018.

  30. Top-k Relevant Semantic Place Retrieval on Spatial RDF Data.
    Jieming Shi, Dingming Wu, Nikos Mamoulis.
    SIGMOD 2016, in the Proceedings of the ACM Conference on Management of Data, San Francisco, CA, June 2016.

  31. Textually Relevant Spatial Skylines.
    Jieming Shi, Dingming Wu, Nikos Mamoulis.
    TKDE 2016, IEEE Transactions on Knowledge and Data Engineering, 2016.

  32. Density-based Place Clustering in Geo-Social Networks.
    Jieming Shi, Nikos Mamoulis, Dingming Wu, David W. Cheung.
    SIGMOD 2014, in the Proceedings of the ACM Conference on Management of Data, Snowbird, UT, June 2014.

Teaching

  • COMP5554, Advanced Artificial Intelligence, 2023/24
  • COMP5434, Big Data Computing, 2021/22, 2022/23, 2023/24
  • COMP2013, Data Structures and Algorithms, 2023/24
  • COMP6434, Big Data Analytics and Artificial Intelligence, 2020/21, 2022/2023
  • COMP3131, Business and Information Systems Strategies, Fall 2020

Professional Service

I am/was a program committee member for the following conferences:
  • PVLDB Volume 16 (VLDB), 2023
  • ACM SIGKDDD Conference on Knowledge Discovery and Data Mining (KDD), 2022
  • IEEE International Conference on Data Engineering (ICDE), 2022 (research and demo tracks)
  • The Web Conference (WWW), 2022
  • The Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI), 2021, 2022
  • International Joint Conference on Artificial Intelligence (IJCAI), 2020, 2021, 2022
  • ACM International Conference on Information and Knowledge Management (CIKM), 2019, 2021
  • The Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), 2020, 2021, 2022
  • The 5th APWeb-WAIM International Joint Conference on Web and Big Data (APWeb-WAIM), 2021
  • The International Symposium on Spatial and Temporal Databases (SSTD), 2021 (Session Chair)
  • IEEE International Conference on Knowledge Graph (ICKG), 2021
  • IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), 2019, 2020
  • International Conference on Web Information Systems Engineering (WISE), 2019

I am/was invited reviewers for the following journals:
  • IEEE Transactions on Knowledge and Data Engineering (TKDE)
  • The International Journal of Very Large Data Bases (VLDBJ)
  • World Wide Web: Internet and Web Information Systems (WWWJ)
  • IEEE Transactions on Big Data (TBD)
  • Information Sciences

Useful Information

Acknowledgement

Our research is supported generously by The Hong Kong Polytechnic University, Hong Kong RGC, Tencent Technology Limited, etc.