Zemin Liu now is an assistant professor in the College of Computer Science and Technology, Zhejiang University. His research interests lie in graph embedding, graph neural networks, and learning on heterogeneous information networks, particularly in investigating the novel research problems such as meta-learning, few-shot learning, and imbalanced learning on graphs. Moreover, he has served as the PC member for top-tier conferences including ICML, NeurIPS, ICLR, ACL, WWW, SIGKDD, AAAI, IJCAI, etc; and the invited reviewer for prestigious journals including TKDE, TOIS, CSUR, etc.

Selected Publications

(* denotes co-first authors; † denotes corresponding author.)


Preprint Papers:

1. Advancing Graph Representation Learning with Large Language Models: A Comprehensive Survey of Techniques
   Qiheng Mao, Zemin Liu, Chenghao Liu, Zhuo Li, Jianling Sun.
   In submission.
    [arXiv]
   

2. Few-Shot Learning on Graphs: from Meta-learning to Pre-training and Prompting
   Xingtong Yu, Yuan Fang, Zemin Liu, Yuxia Wu, Zhihao Wen, Jianyuan Bo, Xinming Zhang, Steven C.H. Hoi.
   In submission.
    [arXiv]
   

3. A Survey of Imbalanced Learning on Graphs: Problems, Techniques, and Future Directions
   Zemin Liu, Yuan Li, Nan Chen, Qian Wang, Bryan Hooi, Bingsheng He.
   In submission.
    [arXiv]  [GitHub]  [Slides]
   

Conference Papers:

1. Full-Attention Driven Graph Contrastive Learning: with Effective Mutual Information Insight
   Long Li, Zemin Liu, Chenghao Liu, Jianling Sun.
   The ACM Web Conference (WWW), 2024.
    [PDF]  [Code]
   

2. ETGraph: A Pioneering Dataset Bridging Ethereum and Twitter
   Qian Wang, Zhen Zhang, Zemin Liu, Shengliang Lu, Bingqiao Luo, Bingsheng He.
   The International Conference on Learning Representations (ICLR), 2024.
    [OpenReview]  [Code]
   

3. Partitioning Message Passing for Graph Fraud Detection
   Wei Zhuo, Zemin Liu, Bryan Hooi, Bingsheng He, Guang Tan, Rizal Fathony, Jia Chen.
   The International Conference on Learning Representations (ICLR), 2024.
    [OpenReview]  [Code]
   

4. Consistency Training with Learnable Data Augmentation for Graph Anomaly Detection with Limited Supervision
   Nan Chen, Zemin Liu, Bryan Hooi, Bingsheng He, Rizal Fathony, Jun Hu, Jia Chen.
   The International Conference on Learning Representations (ICLR), 2024.
    [OpenReview]  [Code]
   

5. HGPROMPT: Bridging Homogeneous and Heterogeneous Graphs for Few-shot Prompt Learning
   Xingtong Yu, Yuan Fang, Zemin Liu, Xinming Zhang.
   The AAAI Conference on Artificial Intelligence (AAAI), 2024.
    [arXiv]  [Code]
   

6. Evaluating Post-hoc Explanations for Graph Neural Networks via Robustness Analysis
   Junfeng Fang, Wei Liu, Yuan Gao, Zemin Liu, An Zhang, Xiang Wang, Xiangnan He.
   Annual Conference on Neural Information Processing Systems (NeurIPS), 2023.
    [OpenReview]  [Code]
   

7. HeteroCS: A Heterogeneous Community Search System With Semantic Explanation
   Weibin Cai, Fanwei Zhu, Zemin Liu, Minghui Wu.
   International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) (Demo), 2023.
    [PDF]  [Video]
   

8. Link Prediction on Latent Heterogeneous Graphs
   Trung-Kien Nguyen*, Zemin Liu*†, Yuan Fang.
   The ACM Web Conference (WWW), 2023.
    [PDF]  [Code]  [arXiv]
   

9. GraphPrompt: Unifying Pre-Training and Downstream Tasks for Graph Neural Networks
   Zemin Liu*, Xingtong Yu*, Yuan Fang, Xinming Zhang.
   The ACM Web Conference (WWW), 2023.
    [PDF]  [Code]  [arXiv]
   

10. HINormer: Representation Learning On Heterogeneous Information Networks with Graph Transformer
    Qiheng Mao, Zemin Liu†, Chenghao Liu†, Jianling Sun.
    The ACM Web Conference (WWW), 2023.
     [PDF]  [Code]  [arXiv]
    

11. Learning to Count Isomorphisms with Graph Neural Networks
    Xingtong Yu*, Zemin Liu*, Yuan Fang, Xinming Zhang.
    The AAAI Conference on Artificial Intelligence (AAAI), 2023.
     [PDF]  [Code]  [Appendix]  [arXiv]
    

12. On Generalized Degree Fairness in Graph Neural Networks
    Zemin Liu, Trung-Kien Nguyen, Yuan Fang.
    The AAAI Conference on Artificial Intelligence (AAAI), 2023.
     [PDF]  [Code]  [Appendix]  [arXiv]
    

13. Cooperative Explanations of Graph Neural Networks
    Junfeng Fang, Xiang Wang, An Zhang, Zemin Liu, Xiangnan He, Tat-Seng Chua.
    ACM International Conference on Web Search and Data Mining (WSDM), 2023.
     [PDF]  [Code]
    

14. LBD: Decouple Relevance and Observation for Individual-Level Unbiased Learning to Rank
    Mouxiang Chen, Chenghao Liu, Zemin Liu, Jianling Sun.
    Annual Conference on Neural Information Processing Systems (NeurIPS), 2022.
     [PDF]  [Code]
    

15. Modeling Price Elasticity for Occupancy Prediction in Hotel Dynamic Pricing
    Fanwei Zhu, Wendong Xiao, Yao Yu, Ziyi Wang, Zulong Chen, Quan Lu, Zemin Liu, Minghui Wu, Shenghua Ni.
    ACM International Conference on Information and Knowledge Management (CIKM) (Short paper), 2022.
     [PDF]
    

16. Scalar is Not Enough: Vectorization-based Unbiased Learning to Rank
    Mouxiang Chen, Chenghao Liu, Zemin Liu, Jianling Sun.
    ACM SIGKDD Conference on Knowledge Discovery and Data Mining (SIGKDD) (Research Track), 2022.
     [PDF]  [Code]

17. Neighbor-Anchoring Adversarial Graph Neural Networks
    Zemin Liu, Yuan Fang, Yong Liu, Vincent W. Zheng.
    IEEE International Conference on Data Engineering (ICDE) (Extended Abstract), 2022.
     [PDF]  [Code]
    

18. On Size-Oriented Long-Tailed Graph Classification of Graph Neural Networks
    Zemin Liu*, Qiheng Mao*, Chenghao Liu, Yuan Fang, Jianling Sun.
    The ACM Web Conference (WWW), 2022.
     [PDF]  [Code]
    

19. Tail-GNN: Tail-Node Graph Neural Networks
    Zemin Liu, Trung-Kien Nguyen, Yuan Fang.
    ACM SIGKDD Conference on Knowledge Discovery and Data Mining (SIGKDD) (Research Track), 2021.
     [PDF]  [Code]
    

20. Node-wise Localization of Graph Neural Networks
    Zemin Liu, Yuan Fang, Chenghao Liu, Steven CH Hoi.
    International Joint Conference on Artificial Intelligence (IJCAI), 2021.
     [PDF]  [Code]  [Appendix]  [arXiv]
    

21. Meta-Inductive Node Classification across Graphs
    Zhihao Wen, Yuan Fang, Zemin Liu.
    International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 2021.
     [PDF]  [Code]
    

22. Relative and Absolute Location Embedding for Few-Shot Node Classification on Graph
    Zemin Liu, Yuan Fang, Chenghao Liu, Steven CH Hoi.
    AAAI Conference on Artificial Intelligence (AAAI), 2021.
     [PDF]  [Code]  [Appendix]
    

23. Towards Locality-Aware Meta-Learning of Tail Node Embeddings on Networks
    Zemin Liu*, Wentao Zhang*, Yuan Fang, Xinming Zhang, Steven CH Hoi.
    ACM International Conference on Information and Knowledge Management (CIKM), 2020.
     [PDF]  [Code]
    

24. Interactive Paths Embedding for Semantic Proximity Search on Heterogeneous Graphs
    Zemin Liu, Vincent W. Zheng, Zhou Zhao, Zhao Li, Hongxia Yang, Minghui Wu, Jing Ying.
    ACM SIGKDD Conference on Knowledge Discovery and Data Mining (SIGKDD) (Research Track), 2018.
     [PDF]  [Code]
    

25. Subgraph-augmented Path Embedding for Semantic User Search on Heterogeneous Social Network
    Zemin Liu, Vincent W. Zheng, Zhou Zhao, Hongxia Yang, Kevin CC Chang, Minghui Wu, Jing Ying.
    The ACM Web Conference (WWW), 2018.
     [PDF]  [Code]
    

26. Distance-Aware DAG Embedding for Proximity Search on Heterogeneous Graphs
    Zemin Liu, Vincent W. Zheng, Zhou Zhao, Fanwei Zhu, Kevin CC Chang, Minghui Wu, Jing Ying.
    AAAI Conference on Artificial Intelligence (AAAI), 2018.
     [PDF]  [Code]
    

27. Topological Recurrent Neural Network for Diffusion Prediction
    Jia Wang, Vincent W. Zheng, Zemin Liu, and Kevin CC Chang.
    IEEE International Conference on Data Mining (ICDM), 2017.
     [PDF]  [Code]
    

28. Semantic Proximity Search on Heterogeneous Graph by Proximity Embedding
    Zemin Liu, Vincent W. Zheng, Zhou Zhao, Fanwei Zhu, Kevin CC Chang, Minghui Wu, Jing Ying.
    AAAI Conference on Artificial Intelligence (AAAI), 2017.
     [PDF]  [Code]
    

Journal Papers:

1. Locality-Aware Tail Node Embeddings on Homogeneous and Heterogeneous Networks
   Zemin Liu, Yuan Fang, Wentao Zhang, Xinming Zhang, Steven CH Hoi.
   IEEE Transactions on Knowledge and Data Engineering (TKDE), 2023.
    [PDF]  [Code]
   

2. How do you visit: Identifying addicts from large-scale transit records via scenario deep embedding
   Canghong Jin, Dongkai Chen, Zhiwei Lin, Zemin Liu, Minghui Wu.
   Geoinformatica (Geoinformatica), 2021.
    [PDF]
   

3. Neighbor-Anchoring Adversarial Graph Neural Networks
   Zemin Liu, Yuan Fang, Yong Liu, Vincent W. Zheng.
   IEEE Transactions on Knowledge and Data Engineering (TKDE), 2021.
    [PDF]  [Code]
   

4. mg2vec: Learning Relationship-Preserving Heterogeneous Graph Representations via Metagraph Embedding
   Wentao Zhang, Yuan Fang, Zemin Liu, Min Wu, Xinming Zhang.
   IEEE Transactions on Knowledge and Data Engineering (TKDE), 2020.
    [PDF]  [Code]
   

Professional Services

Program Committee Member or Reviewer for Conferences:

2019: AAAI (subreviewer), CIKM (subreviewer), ASONAM (subreviewer).
2020: IJCAI, NeurIPS, EMNLP, ACML, PAKDD.
2021: AAAI, ACL, ICML, IJCAI (Senior PC), NeurIPS, PAKDD, ACML, EMNLP, NAACL-HLT.
2022: ICLR, IJCAI, AAAI, ACL, WWW, PAKDD, ICML, SIGKDD, NeurIPS, EMNLP.
2023: AAAI, ACL (Area Chair), EMNLP (Area Chair), WSDM, ICLR, WWW, ICML, NeurIPS, SIGKDD, IJCAI.
2024: WSDM, AAAI, ICLR, WWW, ICML, IJCAI, SIGKDD, ACL.


Reviewer for Journals:

2018: TKDE (IEEE Transactions on Knowledge and Data Engineering).
2020: FCS (Frontiers of Computer Science), IPM (Information Processing and Management).
2021: FCS, Machine Learning.
2022: TKDE, FCS, TMLR (Transactions on Machine Learning Research), CSUR (ACM Computing Surveys).
2023: TKDE, TOIS, CSUR, TNNLS, TMLR.
2024: TKDE, TOIS, CSUR, TMLR.

Useful Links

Shandong University

Zhejiang University

Advanced Digital Sciences Center

Last update: Jun 28, 2021. Webpage template borrows from Prof. Xiangnan He.