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 | 45* | 2024 |
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 163, 122-131, 2023 | 24 | 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 Proceedings of the AAAI Conference on Artificial Intelligence 37 (4), 4391-4399, 2023 | 22 | 2023 |
DisenPOI: Disentangling sequential and geographical influence for point-of-interest recommendation Y Qin, Y Wang, F Sun, W Ju, X Hou, Z Wang, J Cheng, J Lei, M Zhang Proceedings of the Sixteenth ACM International Conference on Web Search and …, 2023 | 20 | 2023 |
DisenCTR: Dynamic graph-based disentangled representation for click-through rate prediction Y Wang, Y Qin, F Sun, B Zhang, X Hou, K Hu, J Cheng, J Lei, M Zhang Proceedings of the 45th International ACM SIGIR Conference on Research and …, 2022 | 16 | 2022 |
Kernel-based substructure exploration for next POI recommendation W Ju, Y Qin, Z Qiao, X Luo, Y Wang, Y Fu, M Zhang 2022 IEEE International Conference on Data Mining (ICDM), 221-230, 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 |
Hope: High-order graph ode for modeling interacting dynamics X Luo, J Yuan, Z Huang, H Jiang, Y Qin, W Ju, M Zhang, Y Sun International Conference on Machine Learning, 23124-23139, 2023 | 13 | 2023 |
Learning on graphs under label noise J Yuan, X Luo, Y Qin, Y Zhao, W Ju, M Zhang ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and …, 2023 | 13 | 2023 |
Towards semi-supervised universal graph classification X Luo, Y Zhao, Y Qin, W Ju, M Zhang IEEE Transactions on Knowledge and Data Engineering, 2023 | 11 | 2023 |
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 | 8 | 2023 |
Alex: Towards effective graph transfer learning with noisy labels J Yuan, X Luo, Y Qin, Z Mao, W Ju, M Zhang Proceedings of the 31st ACM international conference on multimedia, 3647-3656, 2023 | 7 | 2023 |
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 | 7 | 2023 |
Rahnet: Retrieval augmented hybrid network for long-tailed graph classification Z Mao, W Ju, Y Qin, X Luo, M Zhang Proceedings of the 31st ACM International Conference on Multimedia, 3817-3826, 2023 | 6 | 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 | 5 | 2023 |
A Diffusion model for POI recommendation Y Qin, H Wu, W Ju, X Luo, M Zhang ACM Transactions on Information Systems 42 (2), 1-27, 2023 | 5 | 2023 |
Rignn: A rationale perspective for semi-supervised open-world graph classification X Luo, Y Zhao, Z Mao, Y Qin, W Ju, M Zhang, Y Sun Transactions on Machine Learning Research, 2023 | 4 | 2023 |
A Survey on Graph Neural Networks in Intelligent Transportation Systems H Li, Y Zhao, Z Mao, Y Qin, Z Xiao, J Feng, Y Gu, W Ju, X Luo, M Zhang arXiv preprint arXiv:2401.00713, 2024 | 3 | 2024 |
Focus on informative graphs! semi-supervised active learning for graph-level classification W Ju, X Luo, Y Qin, Z Qiao, S Yi, L Liu, Y Fu, M Zhang | 2 | 2023 |
Ad-aug: Adversarial data augmentation for counterfactual recommendation Y Wang, Y Qin, Y Han, M Yin, J Zhou, H Yang, M Zhang Joint European Conference on Machine Learning and Knowledge Discovery in …, 2022 | 2 | 2022 |