Lequan Lin

Orcid: 0009-0006-4677-7327

According to our database1, Lequan Lin authored at least 12 papers between 2022 and 2024.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

Legend:

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Links

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Bibliography

2024
A Simple Yet Effective Framelet-Based Graph Neural Network for Directed Graphs.
IEEE Trans. Artif. Intell., April, 2024

Diffusion models for time-series applications: a survey.
Frontiers Inf. Technol. Electron. Eng., January, 2024

From Continuous Dynamics to Graph Neural Networks: Neural Diffusion and Beyond.
Trans. Mach. Learn. Res., 2024

Diffusing to the Top: Boost Graph Neural Networks with Minimal Hyperparameter Tuning.
CoRR, 2024

When Graph Neural Networks Meet Dynamic Mode Decomposition.
CoRR, 2024

Unleash Graph Neural Networks from Heavy Tuning.
CoRR, 2024

Design Your Own Universe: A Physics-Informed Agnostic Method for Enhancing Graph Neural Networks.
CoRR, 2024

SpecSTG: A Fast Spectral Diffusion Framework for Probabilistic Spatio-Temporal Traffic Forecasting.
CoRR, 2024

Bregman Graph Neural Network.
Proceedings of the IEEE International Conference on Acoustics, 2024

2023
Exposition on over-squashing problem on GNNs: Current Methods, Benchmarks and Challenges.
CoRR, 2023

A Magnetic Framelet-Based Convolutional Neural Network for Directed Graphs.
Proceedings of the IEEE International Conference on Acoustics, 2023

2022
A Simple Yet Effective SVD-GCN for Directed Graphs.
CoRR, 2022


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