3 papers
cs.LG2025
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…
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…