arXiv:2602.18837v2 Announce Type: replace Abstract: Despite their theoretical advantages, spectral methods based on the graph Fourier transform (GFT) are seldom used in graph neural networks (GNNs) due to the cost of computing the eigenbasis and the lack of vertex-domain locality in the resulting representations. As a result, most GNNs rely on local approximations such as polynomial Laplacian filters or message passing, which limit their ability to model long-range dependencies. In this paper, we introduce an exact factorization of the GFT into operators acting on subgraphs, which are then com
The continuous drive for more efficient and powerful AI models necessitates overcoming current computational bottlenecks and theoretical limitations in graph neural networks.
This research could lead to more accurate and generalizable GNNs, enabling breakthroughs in areas like drug discovery, material science, and social network analysis.
By improving GNNs' ability to model long-range dependencies, this work changes the landscape for spectral methods, making them more practically viable for complex AI applications.
- · AI researchers
- · Deep learning framework developers
- · Drug discovery companies
- · AI models relying solely on local message passing
More powerful and theoretically sound graph neural networks become available for a wider range of applications.
Improved GNNs accelerate research in complex systems, leading to advancements in fields like materials science and biomedical informatics.
The enhanced capability of GNNs could contribute to the development of more sophisticated AI agents capable of understanding and navigating highly interconnected data structures.
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Read at arXiv cs.LG