2 citations · 2 across the 5 of their papers we have counts for
5 papers
Transductive Generalization via Optimal Transport and Its Application to Graph Node Classification
MoonJeong Park, Seungbeom Lee, Kyungmin Kim +5
Many existing transductive bounds rely on classical complexity measures that are computationally intractable and often misaligned with empirical behavior. In this work, we establis…
Towards Bridging Generalization and Expressivity of Graph Neural Networks
Shouheng Li, Floris Geerts, Dongwoo Kim +1
Expressivity and generalization are two critical aspects of graph neural networks (GNNs). While significant progress has been made in studying the expressivity of GNNs, much less i…
Generalization of Graph Neural Networks through the Lens of Homomorphism
Shouheng Li, Dongwoo Kim, Qing Wang
Despite the celebrated popularity of Graph Neural Networks (GNNs) across numerous applications, the ability of GNNs to generalize remains less explored. In this work, we propose to…
Local Vertex Colouring Graph Neural Networks
Shouheng Li, Dongwoo Kim, Qing Wang
In recent years, there has been a significant amount of research focused on expanding the expressivity of Graph Neural Networks (GNNs) beyond the Weisfeiler-Lehman (1-WL) framework…
Restructuring Graph for Higher Homophily via Adaptive Spectral Clustering
Shouheng Li, Dongwoo Kim, Qing Wang
While a growing body of literature has been studying new Graph Neural Networks (GNNs) that work on both homophilic and heterophilic graphs, little has been done on adapting classic…