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.
Horn and Charles R
3 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 3representative citing papers
Erlang mixture approximations with the linear chain trick convert distributed delay DDEs into ODEs, with convergence proofs for bounded kernels and applications to stability analysis.
NetSMF enables scalable network embedding by applying spectral sparsification to produce a sparse approximation of the dense matrix implicitly factorized by methods such as DeepWalk.
citing papers explorer
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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 Erlang mixture approximations for differential equations with distributed time delays
Erlang mixture approximations with the linear chain trick convert distributed delay DDEs into ODEs, with convergence proofs for bounded kernels and applications to stability analysis.
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NetSMF: Large-Scale Network Embedding as Sparse Matrix Factorization
NetSMF enables scalable network embedding by applying spectral sparsification to produce a sparse approximation of the dense matrix implicitly factorized by methods such as DeepWalk.