Siyu Yi

Siyu Yi

PostDoc,
School of Mathematics
Sichuan University

Publications

(# Equal Contribution, * Co-corresponding Author)

2026

  1. Wei Ju, Siyu Yi*, Kangjie Zheng, Yifan Wang, Ziyue Qiao, Li Shen, Yongdao Zhou*, Xiaochun Cao, Jiancheng Lv (2026). Compactness and Consistency: A conjoint framework for deep graph clustering. Accepted by the 14th International Conference on Learning Representations.
    ICLR

  2. Wei Ju, Siyu Yi*, Zhengyang Mao, Yifang Qin, Yifan Wang, Zhiping Xiao, Yiwei Fu, Ziyue Qiao, Ming Zhang* (2026). Long-tailed recognition of evidential experts for graph-level classification. Accepted by the ACM on Web Conference 2026.
    WWW, CCF-A

  3. Yifan Wang, Haodong Zhang, Zhiping Xiao, Yusheng Zhao, Siyu Yi, Nan Yin, Xinwang Liu, Ming Zhang, Wei Ju (2026). KEGOD: Kernel-enhanced latent substructure learning for graph out-of-distribution detection. Accepted by the ACM on Web Conference 2026.
    WWW, CCF-A

  4. Wei Zhang, Siyu Yi*, Lezhi Chen, Yifan Wang, Ziyue Qiao, Yongdao Zhou, and Wei Ju* (2026). Evidence-aware integration and domain identification of spatial transcriptomics data. Accepted by the 40th AAAI Conference on Artificial Intelligence.
    [Paper] AAAI, CCF-A

  5. Haodong Zhang, Xinyue Wang, Tao Ren, Yifan Wang, Siyu Yi, Fanchun Meng, Zeyu Ma, QingqingLong, Wei Ju (2026). FairGC: Fostering individual and group fairness for deep graph clustering. Accepted by the 40th AAAI Conference on Artificial Intelligence.
    AAAI, CCF-A

2025

  1. Wei Ju, Siyu Yi*, Yifan Wang, Zhiping Xiao, Zhengyang Mao, Hourun Li, Yiyang Gu, Yifang Qin, Nan Yin, Senzhang Wang, Xinwang Liu, Philip S. Yu, and Ming Zhang* (2025). A survey of graph neural networks in real world: imbalance, noise, privacy and OOD challenges. Accepted by IEEE Transactions on Pattern Analysis and Machine Intelligence. DOI: 10.1109/TPAMI.2025.3630673.
    [Paper] IEEE TPAMI, JCR Q1, 中科院一区, CCF-A, IF=18.6

  2. Siyu Yi, Zhengyang Mao, Kangjie Zheng, Zhiping Xiao, Ziyue Qiao, Chong Chen, Xiansheng Hua, Yongdao Zhou, Ming Zhang, and Wei Ju (2025). Learning generalizable contrastive representations for graph zero-shot learning. IEEE Transactions on Multimedia, 27, 7584-7595. DOI: 10.1109/TMM.2025.3599043.
    [Paper] IEEE TMM, JCR Q1, 中科院一区, IF=8.4

  3. Siyu Yi, Zhengyang Mao, Yifan Wang, Yiyang Gu, Zhiping Xiao, Chong Chen, Xian-Sheng Hua, Ming Zhang, and Wei Ju (2025). Hypergraph consistency learning with relational distillation. IEEE Transactions on Multimedia, 27, 7028-7039. DOI:10.1109/TMM.2025. 3543068.
    [Paper] IEEE TMM, JCR Q1, 中科院一区, IF=8.4

  4. Wei Ju, Zhengyang Mao, Siyu Yi*, Yifang Qin, Yiyang Gu, Zhiping Xiao, Jianhao Shen, Ziyue Qiao, and Ming Zhang* (2025). Cluster-guided contrastive class-imbalanced graph classification. In Proceedings of the 39th AAAI Conference on Artificial Intelligence, 39(11), 11924-11932.
    [Paper] AAAI, CCF-A

  5. Xin Ma, Yifan Wang, Siyu Yi, Wei Ju, Junyu Luo, Yusheng Zhao, Xiao Luo, and Jiancheng Lv (2025). Dual prototype-enhanced contrastive framework for class-imbalanced graph domain adaptation. In Proceedings of The Thirty-Ninth Conference on Neural Information Processing Systems.
    NeurIPS, CCF-A

  6. Xin Ma, Yifan Wang, Siyu Yi, Wei Ju, Bei Wu, Ziyue Qiao, Chenwei Tang, and Jiancheng Lv (2025). PALA: Class-imbalanced graph domain adaptation via prototype-anchored learning and alignment. In Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, 3198-3207. https://doi.org/10.24963/ijcai.2025/356.
    [Paper] IJCAI, CCF-A

  7. Tao Ren, Haodong Zhang, Yifan Wang, Wei Ju, Chengwu Liu, Fanchun Meng, Siyu Yi, and Xiao Luo (2025). MHGC: Multi-scale hard sample mining for contrastive deep graph clustering. Information Processing and Management, 62(4), 104084.
    IPM, JCR Q1，中科院一区，IF=7.4

  8. Zhengyang Mao, Wei Ju, Siyu Yi, Yifan Wang, Zhiping Xiao, Qingqing Long, Nan Yin, Xinwang Liu, and Ming Zhang (2025). Learning Knowledge-diverse Experts for Long-tailed Graph Classification. ACM Transactions on Knowledge Discovery from Data, 19(2), 32.
    ACM TKDD, JCR Q1，IF=4

2024

  1. Siyu Yi, Wei Ju, Yifang Qin, Xiao Luo, Luchen Liu, Yongdao Zhou, and Ming Zhang (2024). Redundancy-free self-supervised relational learning for graph clustering. IEEE Transactions on Neural Networks and Learning Systems, 35(12), 18313-18327.
    [Paper] IEEE TNNLS, JCR Q1，中科院一区，IF=10.4

  2. Wei Ju, Zhengyang Mao, Siyu Yi*, Yifang Qin, Yiyang Gu, Zhiping Xiao, Yifan Wang, Xiao Luo, and Ming Zhang* (2024). Hypergraph-enhanced dual semi-supervised graph classification. In Proceedings of the 41st International Conference on Machine Learning, 22594-22604.
    [Paper] ICML, CCF-A

  3. Wei Ju, Siyu Yi*, Yifan Wang, Qingqing Long, Junyu Luo, Zhiping Xiao, and Ming Zhang*. (2024). A survey of data-efficient graph learning. In Proceedings of the 33rd International Joint Conference on Artificial Intelligence, 8104-8113.
    [Paper] IJCAI, CCF-A

  4. Wei Ju, Yusheng Zhao, Yifang Qin, Siyu Yi, Jingyang Yuan, Zhiping Xiao, Xiao Luo, Xiting Yan, Ming Zhang. (2024). COOL: A conjoint perspective on spatio-temporal graph neural network for traffic forecasting. Information Fusion, 107, 102341.
    JCR Q1，中科院一区，IF=18.6

  5. Wei Ju, Zhengyang Mao, Ziyue Qiao, Yifang Qin, Siyu Yi, Zhiping Xiao, Xiao Luo, Yanjie Fu, Ming Zhang. (2024). Focus on informative graphs! Semi-supervised active learning for graph-level classification. Pattern Recognition, 153, 110567.
    JCR Q1，中科院一区，IF=8

2023

  1. Siyu Yi#, Ze Liu#, Min-Qian Liu, and Yongdao Zhou (2023). Global likelihood sampler for multimodal distributions, Journal of Computational and Graphical Statistics, 32(3), 927-937.
    [Paper] JCR Q1，中科院二区，IF=2.4

  2. Siyu Yi, and Yongdao Zhou (2023). Model-free global likelihood subsampling for massive data. Statistics and Computing, 33(1):9.
    [Paper] JCR Q1，中科院二区，IF=2.2

  3. Siyu Yi#, Zhengyang Mao#, Wei Ju, Yongdao Zhou, Luchen Liu, Xiao Luo, and Ming Zhang (2023). Towards long-tailed recognition for graph classification via collaborative experts. IEEE Transactions on Big Data, 9(6), 1683-1696.
    [Paper] JCR Q1，IF=7.2

  4. Ze Liu#, Siyu Yi#, Jianghu (James) Dong, Min-Qian Liu, and Yongdao Zhou (2023). A sampling scheme for estimating the prevalence of a pandemic. Communications in Statistics - Simulation and Computation, DOI: 10.1080/03610918.2023.2213425.

  5. Xiao Luo, Wei Ju, Yiyang Gu, Yifang Qin, Siyu Yi, Daqing Wu, Luchen Liu, and Ming Zhang (2023). Towards effective semi-supervised node classification with hybrid curriculum pseudo-labeling. ACM Transactions on Multimedia Computing Communications and Applications, 20(3):82.
    JCR Q1, IF=5.1

  6. Wei Ju, Yifang Qin, Siyu Yi, Zhengyang Mao, Kangjie Zheng, Luchen Liu, Xiao Luo, and Ming Zhang (2023). Zero-shot node classification with graph contrastive embedding network. Transactions on Machine Learning Research, online, URL: https://openreview.net/forum?id=8wGXnjRLSy.

2022

  1. Siyu Yi, Yongdao Zhou, and Wei Zheng (2022). Optimal designs for mean-covariance models with missing observations. Journal of Statistical Planning and Inference, 219, 85–97.

  2. Yanping Gao#, Siyu Yi#, and Yongdao Zhou# (2022). Maximin L1-distance range-fixed level-augmented designs. Statistics and Probability Letters, 186, 109470.

2021

  1. Siyu Yi, Yongdao Zhou, and Jianxin Pan (2021). D-optimal designs of mean-covariance models for longitudinal data. Biometrical Journal, 63(5), 1072–1085.
    JCR Q2

  2. Yanping Gao#, Siyu Yi#, and Yongdao Zhou# (2021). Level-augmented uniform designs. Statistical Papers, 63(2), 441–460.

2018

  1. Siyu Yi, and Yongdao Zhou (2018). Projection uniformity under mixture discrepancy. Statistics and Probability Letters, 140, 96–105.