no code implementations • 26 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.
no code implementations • 16 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.
no code implementations • 13 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.
no code implementations • 16 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.
1 code implementation • 12 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.
Ranked #31 on Node Classification on Texas
no code implementations • 1 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.
no code implementations • 20 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.
1 code implementation • 19 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.