| 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, 2022 | 215 | 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 | 133 | 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 | 52 | 2024 |
| When do we need gnn for node classification? S Luan, C Hua, Q Lu, J Zhu, XW Chang, D Precup arXiv preprint arXiv:2210.16979, 2022 | 21 | 2022 |
| 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, 37-48, 2023 | 10 | 2023 |
| 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 | 7 | 2024 |
| Representation learning on heterophilic graph with directional neighborhood attention Q Lu, J Zhu, S Luan, XW Chang arXiv preprint arXiv:2403.01475, 2024 | 6 | 2024 |
| GCEPNet: Graph Convolution-Enhanced Expectation Propagation for Massive MIMO Detection Q Lu, S Luan, XW Chang arXiv preprint arXiv:2404.14886, 2024 | 5 | 2024 |
| Flexible diffusion scopes with parameterized laplacian for heterophilic graph learning Q Lu, J Zhu, S Luan, XW Chang arXiv preprint arXiv:2409.09888, 2024 | 3 | 2024 |
| Are Heterophily-Specific GNNs and Homophily Metrics Really Effective? Evaluation Pitfalls and New Benchmarks S Luan, Q Lu, C Hua, X Wang, J Zhu, XW Chang, G Wolf, J Tang arXiv preprint arXiv:2409.05755, 2024 | 3 | 2024 |
| When do graph neural networks help with node classification? investigating the impact of homophily principle on node distinguishability S Luan, C Hua, M Xu, Q Lu, J Zhu, XW Chang, J Fu, J Leskovec, D Precup arXiv preprint arXiv:2304.14274, 2023 | 3 | 2023 |
| Is Heterophily A Real Nightmare For Graph Neural Networks on Performing Node Classification? S Luan, C Hua, Q Lu, J Zhu, H Zhao, S Zhang, XW Chang, D Precup | 3 | 2021 |
| Bidirectional Generative Pre-training for Improving Time Series Representation Learning Z Song, Q Lu, H Zhu, Y Li arXiv preprint arXiv:2402.09558, 2024 | 1 | 2024 |
| Bidirectional generative pretraining for improving healthcare time-series representation learning Z Song, Q Lu, H Zhu, D Buckeridge, Y Li Machine Learning for Healthcare Conference (MLHC), 2024b. URL https …, 2024 | 1 | 2024 |
| Success Probabilities of L2-norm Regularized Babai Detectors and Maximization XW Chang, Q Lu, Y Xu 2023 IEEE International Symposium on Information Theory (ISIT), 1219-1224, 2023 | 1 | 2023 |
| TimelyGPT: Recurrent Convolutional Transformer for Long Time-series Representation Z Song, Q Lu, H Xu, Y Li | | 2023 |
| A Tutorial on Heterophilic Graph Learning (LoG 2024) S Luan, C Hua, Q Lu | | |
| TrajGPT: Healthcare Time-Series Representation Learning for Trajectory Prediction Z Song, Q Lu, H Zhu, DL Buckeridge, Y Li NeurIPS Workshop on Time Series in the Age of Large Models, 0 | | |
