Stable Filters for Generative Modeling of Graph Signals
Generating signals on graphs requires permutation-equivariant models that exhibit stability with respect to relative structural perturbations. While recent graph-aware Schrödinger bridge models incorporate topology information directly into their reference dynamics, it is unclear how perturbations of the graph propagate through these dynamics and affect the resulting generated distributions. In this paper, we analyze the structural stability of graph-aware continuous-time generative models whose drift combines a graph filter with a learned graph neural network. We derive explicit Wasserstein stability bounds that quantify the effect of relative graph perturbations on the generated distrib...
| Authors | Martin Schmidt, Gonzalo Mateos |
|---|---|
| Primary category | eess.SP |
| Published | 16 September 2026 |
| Last updated at source | 16 September 2026 |
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