13 citations · 23 across the 26 of their papers we have counts for
7 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…
Unifying over-smoothing and over-squashing in graph neural networks: A physics informed approach and beyond
Zhiqi Shao, Dai Shi, Andi Han +3
Graph Neural Networks (GNNs) have emerged as one of the leading approaches for machine learning on graph-structured data. Despite their great success, critical computational challe…
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…
How Curvature Enhance the Adaptation Power of Framelet GCNs
Dai Shi, Yi Guo, Zhiqi Shao +1
Graph neural network (GNN) has been demonstrated powerful in modeling graph-structured data. However, despite many successful cases of applying GNNs to various graph classification…
Frameless Graph Knowledge Distillation
Dai Shi, Zhiqi Shao, Yi Guo +1
Knowledge distillation (KD) has shown great potential for transferring knowledge from a complex teacher model to a simple student model in which the heavy learning task can be acco…