| Dual graph attention networks for deep latent representation of multifaceted social effects in recommender systems Q Wu, H Zhang, X Gao, P He, P Weng, H Gao, G Chen The Web Conference (WWW, Oral), 2019 | 344 | 2019 |
| From canonical correlation analysis to self-supervised graph neural networks H Zhang, Q Wu, J Yan, D Wipf, SY Philip Advances in Neural Information Processing Systems (NeurIPS), 2021 | 201 | 2021 |
| Handling distribution shifts on graphs: An invariance perspective Q Wu, H Zhang, J Yan, D Wipf International Conference on Learning Representations (ICLR), 2022 | 178 | 2022 |
| NodeFormer: A Scalable Graph Structure Learning Transformer for Node Classification Q Wu, W Zhao, Z Li, D Wipf, J Yan Advances in Neural Information Processing Systems (NeurIPS, Spotlight), 2022 | 164 | 2022 |
| Learning Substructure Invariance for Out-of-Distribution Molecular Representations N Yang, K Zeng, Q Wu, X Jia, J Yan Advances in Neural Information Processing Systems (NeurIPS, Spotlight), 2022 | 84 | 2022 |
| Dual sequential prediction models linking sequential recommendation and information dissemination Q Wu, Y Gao, X Gao, P Weng, G Chen ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2019 | 83* | 2019 |
| DIFFormer: Scalable (Graph) Transformers Induced by Energy Constrained Diffusion Q Wu, C Yang, W Zhao, Y He, D Wipf, J Yan International Conference on Learning Representations (ICLR, Spotlight), 2023 | 67 | 2023 |
| Energy-based Out-of-Distribution Detection for Graph Neural Networks Q Wu, Y Chen, C Yang, J Yan International Conference on Learning Representations (ICLR), 2023 | 57 | 2023 |
| Graph Neural Networks are Inherently Good Generalizers: Insights by Bridging GNNs and MLPs C Yang, Q Wu, J Wang, J Yan International Conference on Learning Representations (ICLR), 2023 | 52 | 2023 |
| Towards open-world recommendation: An inductive model-based collaborative filtering approach Q Wu, H Zhang, X Gao, J Yan, H Zha International Conference on Machine Learning (ICML), 2021 | 46 | 2021 |
| SGFormer: Simplifying and Empowering Transformers for Large-Graph Representations Q Wu, W Zhao, C Yang, H Zhang, F Nie, H Jiang, Y Bian, J Yan Advances in Neural Information Processing Systems (NeurIPS), 2023 | 39* | 2023 |
| GraphDE: A Generative Framework for Debiased Learning and Out-of-Distribution Detection on Graphs Z Li, Q Wu, F Nie, J Yan Advances in Neural Information Processing Systems (NeurIPS), 2022 | 36 | 2022 |
| Molerec: Combinatorial drug recommendation with substructure-aware molecular representation learning N Yang, K Zeng, Q Wu, J Yan The Web Conference (WWW), 2023 | 33 | 2023 |
| Towards Open-World Feature Extrapolation: An Inductive Graph Learning Approach Q Wu, C Yang, J Yan Advances in Neural Information Processing Systems (NeurIPS), 2021 | 32 | 2021 |
| Geometric Knowledge Distillation: Topology Compression for Graph Neural Networks C Yang, Q Wu, J Yan Advances in Neural Information Processing Systems (NeurIPS), 2022 | 26 | 2022 |
| Adversarial training model unifying feature driven and point process perspectives for event popularity prediction Q Wu, C Yang, H Zhang, X Gao, P Weng, G Chen ACM International Conference on Information and Knowledge Management (CIKM), 2018 | 24 | 2018 |
| Unleashing the Power of Graph Data Augmentation on Covariate Distribution Shift Y Sui, Q Wu, J Wu, Q Cui, L Li, J Zhou, X Wang, X He Advances in Neural Information Processing Systems (NeurIPS), 2023 | 22 | 2023 |
| Towards Out-of-Distribution Sequential Event Prediction: A Causal Treatment C Yang, Q Wu, Q Wen, Z Zhou, L Sun, J Yan Advances in Neural Information Processing Systems (NeurIPS), 2022 | 21 | 2022 |
| Variational inference for training graph neural networks in low-data regime through joint structure-label estimation D Lao, X Yang, Q Wu, J Yan ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2022 | 18 | 2022 |
| Feature Evolution Based Multi-Task Learning for Collaborative Filtering with Social Trust. Q Wu, L Jiang, X Gao, X Yang, G Chen International Joint Conference on Artificial Intelligence (IJCAI), 2019 | 18 | 2019 |
Junchi YanFIET & Prof., Shanghai Jiao Tong University (2018-), RSM of IBM Research (2011-2018)在 cs.sjtu.edu.cn 的电子邮件经过验证
