| Truth inference in crowdsourcing: Is the problem solved? Y Zheng, G Li, Y Li, C Shan, R Cheng Proceedings of the VLDB Endowment 10 (5), 541-552, 2017 | 481 | 2017 |
| Finding global homophily in graph neural networks when meeting heterophily X Li, R Zhu, Y Cheng, C Shan, S Luo, D Li, W Qian International Conference on Machine Learning, 13242-13256, 2022 | 140 | 2022 |
| How powerful is graph convolution for recommendation? Y Shen, Y Wu, Y Zhang, C Shan, J Zhang, BK Letaief, D Li Proceedings of the 30th ACM international conference on information …, 2021 | 74 | 2021 |
| Reinforcement learning enhanced explainer for graph neural networks C Shan, Y Shen, Y Zhang, X Li, D Li Advances in Neural Information Processing Systems 34, 22523-22533, 2021 | 53 | 2021 |
| CLARE: A semi-supervised community detection algorithm X Wu, Y Xiong, Y Zhang, Y Jiao, C Shan, Y Sun, Y Zhu, PS Yu Proceedings of the 28th ACM SIGKDD conference on knowledge discovery and …, 2022 | 27 | 2022 |
| CoPE: modeling continuous propagation and evolution on interaction graph Y Zhang, Y Xiong, D Li, C Shan, K Ren, Y Zhu Proceedings of the 30th ACM International Conference on Information …, 2021 | 23 | 2021 |
| An End-to-End Deep RL Framework for Task Arrangement in Crowdsourcing Platforms C Shan, N Mamoulis, R Cheng, G Li, X Li, Y Qian 2020 IEEE 36th International Conference on Data Engineering (ICDE), 49-60, 2020 | 22 | 2020 |
| On analyzing graphs with motif-paths X Li, R Cheng, KCC Chang, C Shan, C Ma, H Cao Proceedings of the VLDB Endowment 14 (6), 2021 | 18 | 2021 |
| Seegera: Self-supervised semi-implicit graph variational auto-encoders with masking X Li, T Ye, C Shan, D Li, M Gao Proceedings of the ACM web conference 2023, 143-153, 2023 | 16 | 2023 |
| Recognizing vector graphics without rasterization X Jiang, L Liu, C Shan, Y Shen, X Dong, D Li Advances in Neural Information Processing Systems 34, 24569-24580, 2021 | 14 | 2021 |
| A Crowdsourcing Framework for Collecting Tabular Data C Shan, N Mamoulis, G Li, R Cheng, Z Huang, Y Zheng IEEE Transactions on Knowledge and Data Engineering, 2019 | 11 | 2019 |
| T-crowd: Effective crowdsourcing for tabular data C Shan, N Mamoulis, G Li, R Cheng, Z Huang, Y Zheng 2018 IEEE 34th International Conference on Data Engineering (ICDE), 1316-1319, 2018 | 11 | 2018 |
| Explaining temporal graph models through an explorer-navigator framework W Xia, M Lai, C Shan, Y Zhang, X Dai, X Li, D Li The Eleventh International Conference on Learning Representations, 2022 | 10 | 2022 |
| CAST: a correlation-based adaptive spectral clustering algorithm on multi-scale data X Li, B Kao, C Shan, D Yin, M Ester Proceedings of the 26th ACM SIGKDD International Conference on Knowledge …, 2020 | 9 | 2020 |
| Learning decomposed spatial relations for multi-variate time-series modeling Y Fang, K Ren, C Shan, Y Shen, Y Li, W Zhang, Y Yu, D Li Proceedings of the AAAI Conference on Artificial Intelligence 37 (6), 7530-7538, 2023 | 8 | 2023 |
| COCLEP: Contrastive Learning-based Semi-Supervised Community Search L Li, S Luo, Y Zhao, C Shan, Z Wang, L Qin 2023 IEEE 39th International Conference on Data Engineering (ICDE), 2483-2495, 2023 | 6 | 2023 |
| Motif paths: A new approach for analyzing higher-order semantics between graph nodes X Li, TN Chan, R Cheng, C Shan, C Ma, K Chang HKU Technique Reports 3, 4, 2019 | 5 | 2019 |
| Learning to Optimize LSM-trees: Towards A Reinforcement Learning based Key-Value Store for Dynamic Workloads D Mo, F Chen, S Luo, C Shan Proceedings of the ACM on Management of Data 1 (3), 1-25, 2023 | 4 | 2023 |
| Rendnet: Unified 2d/3d recognizer with latent space rendering R Shi, X Jiang, C Shan, Y Wang, D Li Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022 | 4 | 2022 |
| Cmmd: Cross-metric multi-dimensional root cause analysis S Yan, C Shan, W Yang, B Xu, D Li, L Qiu, J Tong, Q Zhang Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and …, 2022 | 3 | 2022 |
