arXiv AI
Finite-Time Node Separation in Recurrent Graph Neural Networks with Persistent Gaussian Perturbations
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Persistent Gaussian Perturbations Prevent Oversmoothing in Recurrent Graph Neural Networks
arXiv:2607. 28185v1 Announce Type: new Abstract: Oversmoothing is a fundamental limitation of deep graph neural networks (GNNs), where repeated message passing causes node representations to become increasingly similar, eventually collapsing toward a low-dimensional subspace.
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Persistent Gaussian Perturbations Prevent Oversmoothing in Recurrent Graph Neural Networks
Oversmoothing is a fundamental limitation of deep graph neural networks (GNNs), where repeated message passing causes node representations to become increasingly similar, eventually collapsing toward a low-dimensional subspace. This phenomenon limits the effective depth of message-passing architectures and motivates the search for mechanisms that preserve representation diversity.
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Graphon Particle Systems, Part II: Dynamics of Distributed Stochastic Continuum Optimization
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On the Rademacher Complexity of Graph Neural Networks: Unifying Expressivity and Geometry
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