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Randomized ising models for graph node representation
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DOI：10.1016/j.neucom.2026.133195.png)
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Abstract
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• Novel graph reservoir based on Ising models, amenable to physical implementations. • Theorems for convergence at zero temperature and in the presence of thermal noise. • Accuracy on node classification tasks compatible with GESNs and fully-trained GNNs. • Analysis of the robustness to thermal noise and impact of reservoir designs.
Keywords:
Ising models
graph reservoir
node classification
thermal noise
graph representation
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6.5
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2.5W
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6.5W
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NATURE PHOTONICS
NATURE PHOTONICSIF32.9
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