Introduces graph-to-image prediction of per-node dynamic stability landscapes in oscillator networks from topology, releases two 10k-graph datasets, and shows GNN-CNN models achieve good accuracy with cross-size generalization.
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2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
UNVERDICTED 2representative citing papers
Defines multiple digraph Laplacian matrices and proves spectral relations, a radius bound with equality case, and a determinantal identity linking regular digraphs to their line digraphs.
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Learning Dynamic Stability Landscapes in Synchronization Networks
Introduces graph-to-image prediction of per-node dynamic stability landscapes in oscillator networks from topology, releases two 10k-graph datasets, and shows GNN-CNN models achieve good accuracy with cross-size generalization.
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On the Spectra of Digraph Laplacians
Defines multiple digraph Laplacian matrices and proves spectral relations, a radius bound with equality case, and a determinantal identity linking regular digraphs to their line digraphs.