Ultrametric graphons model hierarchical community networks and yield closed-form Laplacian spectra that approximate those of sampled random graphs with high probability as hierarchy depth grows.
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2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 2verdicts
UNVERDICTED 2representative citing papers
RNN computation is recovered from multi-hop graph pathways, and constraining these pathways via resolvent regularization yields improved temporal sparsity and task performance over standard L1.
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Ultrametric Graphons and Hierarchical Community Networks: Spectral Theory and Applications
Ultrametric graphons model hierarchical community networks and yield closed-form Laplacian spectra that approximate those of sampled random graphs with high probability as hierarchy depth grows.
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Unifying Dynamical Systems and Graph Theory to Mechanistically Understand Computation in Neural Networks
RNN computation is recovered from multi-hop graph pathways, and constraining these pathways via resolvent regularization yields improved temporal sparsity and task performance over standard L1.