From continuous dynamics to graph neural networks: Neural diffusion and beyond A Han, D Shi, L Lin, J Gao arXiv preprint arXiv:2310.10121, 2023 | 20 | 2023 |
Quasi-framelets: Another improvement to graphneural networks M Yang, X Zheng, J Yin, J Gao arXiv preprint arXiv:2201.04728, 2022 | 14 | 2022 |
Unifying over-smoothing and over-squashing in graph neural networks: A physics informed approach and beyond Z Shao, D Shi, A Han, Y Guo, Q Zhao, J Gao arXiv preprint arXiv:2309.02769, 2023 | 12 | 2023 |
How curvature enhance the adaptation power of framelet gcns D Shi, Y Guo, Z Shao, J Gao arXiv preprint arXiv:2307.09768, 2023 | 12 | 2023 |
Generalized energy and gradient flow via graph framelets A Han, D Shi, Z Shao, J Gao arXiv preprint arXiv:2210.04124, 2022 | 12 | 2022 |
Coupling matrix manifolds assisted optimization for optimal transport problems D Shi, J Gao, X Hong, ST Boris Choy, Z Wang Machine Learning 110, 533-558, 2021 | 10 | 2021 |
Enhancing framelet GCNs with generalized p-Laplacian regularization Z Shao, D Shi, A Han, A Vasnev, Y Guo, J Gao International Journal of Machine Learning and Cybernetics 15 (4), 1553-1573, 2024 | 9* | 2024 |
Exposition on over-squashing problem on GNNs: Current Methods, Benchmarks and Challenges D Shi, A Han, L Lin, Y Guo, J Gao arXiv preprint arXiv:2311.07073, 2023 | 8 | 2023 |
Design your own universe: A physics-informed agnostic method for enhancing graph neural networks D Shi, A Han, L Lin, Y Guo, Z Wang, J Gao International Journal of Machine Learning and Cybernetics, 1-16, 2024 | 7 | 2024 |
Specstg: A fast spectral diffusion framework for probabilistic spatio-temporal traffic forecasting L Lin, D Shi, A Han, J Gao arXiv preprint arXiv:2401.08119, 2024 | 7 | 2024 |
Revisiting Generalized p-Laplacian Regularized Framelet GCNs: Convergence, Energy Dynamic and as Non-Linear Diffusion D Shi, Z Shao, Y Guo, Q Zhao, J Gao Transactions on Machine Learning Research, 0 | 5* | |
Bregman Graph Neural Network J Zhai, L Lin, D Shi, J Gao arXiv preprint arXiv:2309.06645, 2023 | 4 | 2023 |
Frameless graph knowledge distillation D Shi, Z Shao, J Gao, Z Wang, Y Guo IEEE Transactions on Neural Networks and Learning Systems, 2024 | 3 | 2024 |
Quasi-framelets: robust graph neural networks via adaptive framelet convolution M Yang, D Shi, X Zheng, J Yin, J Gao International Journal of Machine Learning and Cybernetics, 1-16, 2024 | 2 | 2024 |
Fixed Point Laplacian Mapping: A Geometrically Correct Manifold Learning Algorithm D Shi, A Han, Y Guo, J Gao 2023 International Joint Conference on Neural Networks (IJCNN), 1-9, 2023 | 2 | 2023 |
Unleash Graph Neural Networks from Heavy Tuning L Lin, D Shi, A Han, Z Wang, J Gao arXiv preprint arXiv:2405.12521, 2024 | 1 | 2024 |
A New Perspective On the Expressive Equivalence Between Graph Convolution and Attention Models D Shi, Z Shao, A Han, Y Guo, G Junbin Asian Conference on Machine Learning, 1199-1214, 2024 | 1 | 2024 |
Generalized Laplacian Regularized Framelet Graph Neural Networks Z Shao, A Han, D Shi, A Vasnev, J Gao arXiv preprint arXiv:2210.15092, 2022 | 1 | 2022 |
A discussion on the validity of manifold learning D Shi, A Han, Y Guo, J Gao arXiv preprint arXiv:2106.01608, 2021 | 1 | 2021 |
Diffusing to the Top: Boost Graph Neural Networks with Minimal Hyperparameter Tuning L Lin, D Shi, A Han, Z Wang, J Gao arXiv preprint arXiv:2410.05697, 2024 | | 2024 |