| A survey of graph neural networks in real world: Imbalance, noise, privacy and ood challenges W Ju, S Yi, Y Wang, Z Xiao, Z Mao, H Li, Y Gu, Y Qin, N Yin, S Wang, ... arXiv preprint arXiv:2403.04468, 2024 | 36 | 2024 |
| Redundancy-free self-supervised relational learning for graph clustering S Yi, W Ju, Y Qin, X Luo, L Liu, Y Zhou, M Zhang IEEE Transactions on Neural Networks and Learning Systems, 2023 | 23 | 2023 |
| A survey of data-efficient graph learning W Ju, S Yi, Y Wang, Q Long, J Luo, Z Xiao, M Zhang Accepted by IJCAI24, arXiv preprint arXiv:2402.00447, 2024 | 20* | 2024 |
| Zero-shot node classification with graph contrastive embedding network W Ju, Y Qin, S Yi, Z Mao, K Zheng, L Liu, X Luo, M Zhang Transactions on Machine Learning Research, 2023 | 17 | 2023 |
| Toward effective semi-supervised node classification with hybrid curriculum pseudo-labeling X Luo, W Ju, Y Gu, Y Qin, S Yi, D Wu, L Liu, M Zhang ACM Transactions on Multimedia Computing, Communications and Applications 20 …, 2023 | 12 | 2023 |
| Projection uniformity under mixture discrepancy SY Yi, YD Zhou Statistics & Probability Letters 140, 96-105, 2018 | 11 | 2018 |
| Cool: a conjoint perspective on spatio-temporal graph neural network for traffic forecasting W Ju, Y Zhao, Y Qin, S Yi, J Yuan, Z Xiao, X Luo, X Yan, M Zhang Information Fusion 107, 102341, 2024 | 9 | 2024 |
| Towards long-tailed recognition for graph classification via collaborative experts SY Yi, Z Mao, W Ju, YD Zhou, L Liu, X Luo, M Zhang IEEE Transactions on Big Data, 2023 | 7 | 2023 |
| Model-free global likelihood subsampling for massive data SY Yi, YD Zhou Statistics and Computing 33 (1), 9, 2023 | 7 | 2023 |
| Towards Graph Contrastive Learning: A Survey and Beyond W Ju, Y Wang, Y Qin, Z Mao, Z Xiao, J Luo, J Yang, Y Gu, D Wang, ... arXiv preprint arXiv:2405.11868, 2024 | 6 | 2024 |
| Global likelihood sampler for multimodal distributions SY Yi, Z Liu, MQ Liu, YD Zhou Journal of Computational and Graphical Statistics 32 (3), 927-937, 2023 | 6 | 2023 |
| Hypergraph-enhanced Dual Semi-supervised Graph Classification W Ju, Z Mao, S Yi, Y Qin, Y Gu, Z Xiao, Y Wang, X Luo, M Zhang Accepted by ICML24, arXiv preprint arXiv:2405.04773, 2024 | 5 | 2024 |
| Level-augmented uniform designs YP Gao, SY Yi, YD Zhou Statistical Papers 63 (2), 441-460, 2022 | 4 | 2022 |
| Focus on informative graphs! Semi-supervised active learning for graph-level classification W Ju, Z Mao, Z Qiao, Y Qin, S Yi, Z Xiao, X Luo, Y Fu, M Zhang Pattern Recognition 153, 110567, 2024 | 3 | 2024 |
| D‐optimal designs of mean‐covariance models for longitudinal data S Yi, Y Zhou, J Pan Biometrical Journal 63 (5), 1072-1085, 2021 | 2 | 2021 |
| Maximin L1-distance Range-fixed Level-augmented designs Y Gao, S Yi, Y Zhou Statistics & Probability Letters 186, 109470, 2022 | 1 | 2022 |
| Evidential Self-Supervised Graph Representation Learning via Prototype-based Consistency W Ju, S Yi, M Zhang Proceedings of the ACM Turing Award Celebration Conference-China 2024, 210-211, 2024 | | 2024 |
| Learning Knowledge-diverse Experts for Long-tailed Graph Classification Z Mao, W Ju, S Yi, Y Wang, Z Xiao, Q Long, N Yin, X Liu, M Zhang ACM Transactions on Knowledge Discovery from Data, 2024 | | 2024 |
| A sampling scheme for estimating the prevalence of a pandemic Z Liu, SY Yi, J Dong, MQ Liu, YD Zhou Communications in Statistics-Simulation and Computation, 1-17, 2023 | | 2023 |
| Optimal designs for mean–covariance models with missing observations SY Yi, YD Zhou, W Zheng Journal of Statistical Planning and Inference 219, 85-97, 2022 | | 2022 |
Wei Ju（琚玮）Associate Professor, Sichuan UniversityVerified email at scu.edu.cn
