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We consider the controllability of large-scale linear networked dynamicalsystems when complete knowledge of network structure is unavailable andknowledge is limited to coarse summaries. We provide conditions under whichaverage controllability of the fine-scale system can be well approximated byaverage controllability of the (synthesized, reduced-order) coarse-scalesystem. To this end, we require knowledge of some inherent parametric structureof the fine-scale network that makes this type of approximation possible.Therefore, we assume that the underlying fine-scale network is generated by thestochastic block model (SBM) -- often studied in community detection. We thenprovide an algorithm that directly estimates the average controllability of thefine-scale system using a coarse summary of SBM. Our analysis indicates thenecessity of underlying structure (e.g., in-built communities) to be able toquantify accurately the controllability from coarsely characterized networkeddynamics. We also compare our method to that of the reduced-order method andhighlight the regimes where both can outperform each other. Finally, we providesimulations to confirm our theoretical results for different scalings ofnetwork size and density, and the parameter that captures how muchcommunity-structure is retained in the coarse summary.

Controllability of Coarsely Measured Networked Linear Dynamical Systems (Extended Version)