Search Results for author: Lequan Lin

Found 8 papers, 2 papers with code

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

no code implementations26 Jan 2024 Dai Shi, Andi Han, Lequan Lin, Yi Guo, Zhiyong Wang, Junbin Gao

Physics-informed Graph Neural Networks have achieved remarkable performance in learning through graph-structured data by mitigating common GNN challenges such as over-smoothing, over-squashing, and heterophily adaption.

SpecSTG: A Fast Spectral Diffusion Framework for Probabilistic Spatio-Temporal Traffic Forecasting

no code implementations16 Jan 2024 Lequan Lin, Dai Shi, Andi Han, Junbin Gao

Our method generates the Fourier representation of future time series, transforming the learning process into the spectral domain enriched with spatial information.

Time Series

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

no code implementations13 Nov 2023 Dai Shi, Andi Han, Lequan Lin, Yi Guo, Junbin Gao

Graph-based message-passing neural networks (MPNNs) have achieved remarkable success in both node and graph-level learning tasks.

From Continuous Dynamics to Graph Neural Networks: Neural Diffusion and Beyond

no code implementations16 Oct 2023 Andi Han, Dai Shi, Lequan Lin, Junbin Gao

Such a scheme has been found to be intrinsically linked to a physical process known as heat diffusion, where the propagation of GNNs naturally corresponds to the evolution of heat density.

Bregman Graph Neural Network

1 code implementation12 Sep 2023 Jiayu Zhai, Lequan Lin, Dai Shi, Junbin Gao

Numerous recent research on graph neural networks (GNNs) has focused on formulating GNN architectures as an optimization problem with the smoothness assumption.

Bilevel Optimization Node Classification

Diffusion Models for Time Series Applications: A Survey

no code implementations1 May 2023 Lequan Lin, Zhengkun Li, Ruikun Li, Xuliang Li, Junbin Gao

Diffusion models, a family of generative models based on deep learning, have become increasingly prominent in cutting-edge machine learning research.

Imputation Time Series +1

A Magnetic Framelet-Based Convolutional Neural Network for Directed Graphs

no code implementations20 Oct 2022 Lequan Lin, Junbin Gao

Spectral Graph Convolutional Networks (spectral GCNNs), a powerful tool for analyzing and processing graph data, typically apply frequency filtering via Fourier transform to obtain representations with selective information.

Denoising Link Prediction +1

A Simple Yet Effective SVD-GCN for Directed Graphs

1 code implementation19 May 2022 Chunya Zou, Andi Han, Lequan Lin, Junbin Gao

In this paper, we propose a simple yet effective graph neural network for directed graphs (digraph) based on the classic Singular Value Decomposition (SVD), named SVD-GCN.

Denoising Node Classification

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