| Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks S Luan, M Zhao, XW Chang, D Precup Advances in Neural Information Processing Systems 32, 2019 | 187 | 2019 |
| Revisiting heterophily for graph neural networks S Luan, C Hua, Q Lu, J Zhu, M Zhao, S Zhang, XW Chang, D Precup Advances in neural information processing systems 35, 1362-1375 (Spotlight), 2022 | 164 | 2022 |
| Is heterophily a real nightmare for graph neural networks to do node classification? S Luan, C Hua, Q Lu, J Zhu, M Zhao, S Zhang, XW Chang, D Precup arXiv preprint arXiv:2109.05641, 2021 | 119 | 2021 |
| Complete the missing half: Augmenting aggregation filtering with diversification for graph convolutional networks S Luan, M Zhao, C Hua, XW Chang, D Precup In NeurIPS 2022 Workshop: New Frontiers in Graph Learning (Oral), 2020 | 43 | 2020 |
| A consciousness-inspired planning agent for model-based reinforcement learning M Zhao, Z Liu, S Luan, S Zhang, D Precup, Y Bengio Advances in neural information processing systems 34, 1569-1581, 2021 | 41 | 2021 |
| When Do Graph Neural Networks Help with Node Classification? Investigating the Homophily Principle on Node Distinguishability S Luan, C Hua, M Xu, Q Lu, J Zhu, XW Chang, J Fu, J Leskovec, D Precup Advances in Neural Information Processing Systems 36, 2024 | 37 | 2024 |
| When Do We Need Graph Neural Networks for Node Classification? S Luan, C Hua, Q Lu, J Zhu, XW Chang, D Precup International Conference on Complex Networks and Their Applications, 2022 | 26* | 2022 |
| Mudiff: Unified diffusion for complete molecule generation C Hua, S Luan, M Xu, Z Ying, J Fu, S Ermon, D Precup Learning on Graphs Conference, 33: 1-33: 26, 2024 | 24 | 2024 |
| Training matters: Unlocking potentials of deeper graph convolutional neural networks S Luan, M Zhao, XW Chang, D Precup International Conference on Complex Networks and Their Applications (Oral …, 2023 | 16 | 2023 |
| META-Learning State-based Eligibility Traces for More Sample-Efficient Policy Evaluation M Zhao, S Luan, I Porada, X Chang, D Precup In Proceedings of the 19th International Conference on Autonomous Agents and …, 2019 | 8 | 2019 |
| Graph Neural Networks Intersect Probabilistic Graphical Models: A Survey C Hua, S Luan, Q Zhang, J Fu arXiv preprint arXiv:2206.06089, 2022 | 7 | 2022 |
| Revisit Policy Optimization in Matrix Form S Luan, XW Chang, D Precup NeurIPS 2019 Optimization Foundations for Reinforcement Learning Workshop, 2019 | 6 | 2019 |
| Representation Learning on Heterophilic Graph with Directional Neighborhood Attention Q Lu, J Zhu, S Luan, XW Chang arXiv preprint arXiv:2403.01475, 2024 | 4 | 2024 |
| Addressing the Limitations of Graph Neural Networks on Node-level Tasks S Luan McGill University, 2024 | 4* | 2024 |
| Multi-Dataset Multi-Task Framework for Learning Molecules and Protein-target Interactions Properties C Hua, S Luan, J Fu, D Precup | 2 | 2022 |
| What Is Missing In Homophily? Disentangling Graph Homophily For Graph Neural Networks Y Zheng, S Luan, L Chen arXiv preprint arXiv:2406.18854, 2024 | 1 | 2024 |
| GCEPNet: Graph Convolution-Enhanced Expectation Propagation for Massive MIMO Detection Q Lu, S Luan, XW Chang arXiv preprint arXiv:2404.14886, 2024 | 1 | 2024 |
| The Heterophilic Graph Learning Handbook: Benchmarks, Models, Theoretical Analysis, Applications and Challenges S Luan, C Hua, Q Lu, L Ma, L Wu, X Wang, M Xu, XW Chang, D Precup, ... arXiv preprint arXiv:2407.09618, 2024 | | 2024 |
| Pedestrian Detection in Aerial Image Based on Convolutional Neural Network with Attention Mechanism and Multi-scale Prediction J Yang, Q Zhang, Y Chen, S Luan Preprints, 2024 | | 2024 |
Doina PrecupDeepMind and McGill UniversityVerified email at cs.mcgill.ca
