1 code implementation • 10 Mar 2024 • Shouheng Li, Dongwoo Kim, Qing Wang
Specifically, we propose a new vertex colouring scheme and demonstrate that classical search algorithms can efficiently compute graph representations that extend beyond the 1-WL.
no code implementations • 10 Mar 2024 • Shouheng Li, Dongwoo Kim, Qing Wang
In this work, we propose to study the generalization of GNNs through a novel perspective - analyzing the entropy of graph homomorphism.
no code implementations • The Eleventh International Conference on Learning Representations 2023 • Qing Wang, Dillon Chen, Asiri Wijesinghe, Shouheng Li, Muhammad Farhan
The expressive power of Graph Neural Networks (GNNs) is fundamental for understanding their capabilities and limitations, i. e., what graph properties can or cannot be learnt by a GNN.
no code implementations • 6 Jun 2022 • 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 classical GNNs to less-homophilic graphs.
Ranked #27 on Node Classification on Actor
no code implementations • 1 Jan 2021 • Shouheng Li, Dongwoo Kim, Qing Wang
The proposed model has been shown to generalize well to both assortative and disassortative graphs.