3 papers
cs.LG2024
Understanding Oversmoothing in Diffusion-Based GNNs From the Perspective of Operator Semigroup Theory
Weichen Zhao, Chenguang Wang, Xinyan Wang +3
This paper presents an analytical study of the oversmoothing issue in diffusion-based Graph Neural Networks (GNNs). Generalizing beyond extant approaches grounded in random walk an…
cs.LG2023
ASP: Learn a Universal Neural Solver!
Chenguang Wang, Zhouliang Yu, Stephen McAleer +2
Applying machine learning to combinatorial optimization problems has the potential to improve both efficiency and accuracy. However, existing learning-based solvers often struggle…
cs.LG2022
Comprehensive Analysis of Over-smoothing in Graph Neural Networks from Markov Chains Perspective
Weichen Zhao, Chenguang Wang, Congying Han +1
The over-smoothing problem is an obstacle of developing deep graph neural network (GNN). Although many approaches to improve the over-smoothing problem have been proposed, there is…