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cs.LG2024
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…
cs.LG2024
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…
cs.LG2024
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…