Generalization of Geometric Graph Neural Networks With Lipschitz Loss Functions
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DOI: 10.1109/tsp.2025.3553378
Abstract: In this paper, we study the generalization capabilities of geometric graph neural networks (GNNs). We consider GNNs over a geometric graph constructed from a finite set of randomly sampled points over an embedded manifold with… read more here.
Keywords: geometric graph; graph neural; sampled points; generalization ... See more keywords