9 citations · 15 across the 18 of their papers we have counts for
4 papers · 1 filter
Exposition on over-squashing problem on GNNs: Current Methods, Benchmarks and Challenges
Dai Shi, Andi Han, Lequan Lin +2
Graph-based message-passing neural networks (MPNNs) have achieved remarkable success in both node and graph-level learning tasks. However, several identified problems, including ov…
From Continuous Dynamics to Graph Neural Networks: Neural Diffusion and Beyond
Andi Han, Dai Shi, Lequan Lin +1
Graph neural networks (GNNs) have demonstrated significant promise in modelling relational data and have been widely applied in various fields of interest. The key mechanism behind…
Bregman Graph Neural Network
Jiayu Zhai, Lequan Lin, Dai Shi +1
Numerous recent research on graph neural networks (GNNs) has focused on formulating GNN architectures as an optimization problem with the smoothness assumption. However, in node cl…
Diffusion Models for Time Series Applications: A Survey
Lequan Lin, Zhengkun Li, Ruikun Li +2
Diffusion models, a family of generative models based on deep learning, have become increasingly prominent in cutting-edge machine learning research. With a distinguished performan…