| A survey on deep hashing methods X Luo, H Wang, D Wu, C Chen, M Deng, J Huang, XS Hua ACM Transactions on Knowledge Discovery from Data (TKDD) 17 (1), 1-50, 2023 | 129 | 2023 |
| A comprehensive survey on deep graph representation learning W Ju, Z Fang, Y Gu, Z Liu, Q Long, Z Qiao, Y Qin, J Shen, F Sun, Z Xiao, ... Neural Networks, 106207, 2024 | 44* | 2024 |
| Dualgraph: Improving semi-supervised graph classification via dual contrastive learning X Luo, W Ju, M Qu, C Chen, M Deng, XS Hua, M Zhang ICDE 2022, 2022 | 34 | 2022 |
| Cimon: Towards high-quality hash codes X Luo, D Wu, Z Ma, C Chen, M Deng, J Ma, Z Jin, J Huang, XS Hua IJCAI 2021, 2021 | 33 | 2021 |
| Ghnn: Graph harmonic neural networks for semi-supervised graph-level classification W Ju, X Luo, Z Ma, J Yang, M Deng, M Zhang Neural Networks 151, 70-79, 2022 | 31 | 2022 |
| Unsupervised graph-level representation learning with hierarchical contrasts W Ju, Y Gu, X Luo, Y Wang, H Yuan, H Zhong, M Zhang Neural Networks 158, 359-368, 2023 | 29 | 2023 |
| Expectation pooling: an effective and interpretable pooling method for predicting DNA–protein binding X Luo, X Tu, Y Ding, G Gao, M Deng Bioinformatics 36 (5), 1405-1412, 2020 | 29 | 2020 |
| Clear: Cluster-enhanced contrast for self-supervised graph representation learning X Luo, W Ju, M Qu, Y Gu, C Chen, M Deng, XS Hua, M Zhang IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2022 | 27 | 2022 |
| Few-shot molecular property prediction via Hierarchically Structured Learning on Relation Graphs W Ju, Z Liu, Y Qin, B Feng, C Wang, Z Guo, X Luo, M Zhang Neural Networks, 2023 | 24 | 2023 |
| TGNN: A joint semi-supervised framework for graph-level classification W Ju, X Luo, M Qu, Y Wang, C Chen, M Deng, XS Hua, M Zhang IJCAI 2022, 2022 | 24 | 2022 |
| A statistical approach to mining semantic similarity for deep unsupervised hashing X Luo, D Wu, Z Ma, C Chen, M Deng, J Huang, XS Hua MM 2021, 2021 | 24 | 2021 |
| CoCo: A Coupled Contrastive Framework for Unsupervised Domain Adaptive Graph Classification N Yin, L Shen, M Wang, L Lan, Z Ma, C Chen, XS Hua, X Luo ICML 2023, 2023 | 23* | 2023 |
| Glcc: A general framework for graph-level clustering W Ju, Y Gu, B Chen, G Sun, Y Qin, X Liu, X Luo, M Zhang AAAI 2023, 2023 | 22 | 2023 |
| Dynamic hypergraph convolutional network N Yin, F Feng, Z Luo, X Zhang, W Wang, X Luo, C Chen, XS Hua ICDE 2022, 2022 | 22 | 2022 |
| Dynamic Hypergraph Structure Learning for Traffic Flow Forecasting Y Zhao, X Luo, W Ju, C Chen, XS Hua, M Zhang ICDE 2023, 2023 | 19 | 2023 |
| DEAL: An Unsupervised Domain Adaptive Framework for Graph-level Classification N Yin, L Shen, B Li, M Wang, X Luo, C Chen, Z Luo, XS Hua MM 2022, 2022 | 18 | 2022 |
| OMG: Towards Effective Graph Classification Against Label Noise N Yin, L Shen, M Wang, X Luo, Z Luo, D Tao IEEE Transactions on Knowledge and Data Engineering (TKDE), 2023 | 15 | 2023 |
| Kernel-based Substructure Exploration for Next POI Recommendation W Ju, Y Qin, Z Qiao, X Luo, Y Wang, Y Fu, M Zhang ICDM 2022, Best Paper Finalist, 2022 | 15 | 2022 |
| Learning graph ODE for continuous-time sequential recommendation Y Qin, W Ju, H Wu, X Luo, M Zhang IEEE Transactions on Knowledge and Data Engineering, 2024 | 14 | 2024 |
| Deepprune: Learning efficient and interpretable convolutional networks through weight pruning for predicting dna-protein binding X Luo, W Chi, M Deng Frontiers in genetics 10, 491339, 2019 | 14 | 2019 |
Ming Zhang（张铭）Professor,Peking UniversityVerified email at pku.edu.cn
