1.1k citations · 1.1k across the 5 of their papers we have counts for
5 papers
Asymmetric Learning for Spectral Graph Neural Networks
Fangbing Liu, Qing Wang
Optimizing spectral graph neural networks (GNNs) remains a critical challenge in the field, yet the underlying processes are not well understood. In this paper, we investigate the…
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…
Permutation-Invariant Graph Partitioning:How Graph Neural Networks Capture Structural Interactions?
Asela Hevapathige, Qing Wang
Graph Neural Networks (GNNs) have paved the way for being a cornerstone in graph-related learning tasks. Yet, the ability of GNNs to capture structural interactions within graphs r…
Machine learning accelerated computational fluid dynamics
Dmitrii Kochkov, Jamie A. Smith, Ayya Alieva +3
Numerical simulation of fluids plays an essential role in modeling many physical phenomena, such as weather, climate, aerodynamics and plasma physics. Fluids are well described by…