Stability of Flow Models for Graph Signals
Generating signals on graphs requires permutation-equivariant models that exhibit stability with respect to relative structural perturbations. While favorable stability properties of Graph Neural Networks (GNNs) have been well documented, it is unclear how structural errors propagate through the dynamics of continuous generative flow models that are gaining traction for graph signal generation. In this paper, we analyze continuous normalized flow models parameterized by GNNs and show that permutation equivariance is preserved for both the resulting continuous-time ordinary differential equations and their discrete numerical approximations used as graph signal samplers. Our primary contrib...
| Authors | Martin Schmidt, Gonzalo Mateos |
|---|---|
| Primary category | eess.SP |
| Published | 8 July 2026 |
| Last updated at source | 8 July 2026 |
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