arXiv:2610.34070v1 Announce Type: new Abstract: Graph-signal estimation is commonly designed assuming that the underlying topology is known. In this paper, we consider estimation from general nonlinear measurements when the active topology is latent but is known to belong to a given family of candidate graphs. We first develop a class of topology-blind graph-spectral estimators that jointly exploit the candidate graphs, and characterize the performance loss compared to unconstrained linear estimation. We derive the minimum-mean-squared-error (MSE) multi-basis estimator and show that its optimal operator is the weighted projection of the topology-blind linear MMSE (LMMSE) operator onto the multi-graph filter space. This characterization provides exact expressions for the excess MSE and conditions for attaining the topology-blind LMMSE. To reduce the number of parameters, we develop estimators based on shared spectral responses and low-rank representations, and consider data-dependent estimators that infer the topology from the measurements, by maximum a-posteriori probability (MAP) selection or posterior weighting (multiple-model). We further extend the framework to networks of different sizes by jointly optimizing a common spectral function. We derive its MSE-optimal coefficients and decompose the resulting estimation loss into graph-filter approximation error and the additional cost of sharing a spectral response across networks. The proposed estimators admit sample-based implementations, while the shared spectral function can be applied to unseen graphs without retraining its coefficients. Simulations on IEEE power networks demonstrate the benefits of the proposed graph-spectral estimators under topology uncertainty and cross- network transfer.