8 citations · 32 across the 42 of their papers we have counts for
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cs.LG2023
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…
cs.LG2023★ 2 cited
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…
cs.LG2023★ 4 cited
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…