Deterministic Frequency--Domain Inference of Network Topology and Hidden Components via Structure--Behavior Scaling

Abstract

Hidden interactions and components in complex systems-ranging from covert actors in terrorist networks to unobserved brain regions and molecular regulators-often manifest only through indirect behavioral signals. Inferring the underlying network structure from such partial observations remains a fundamental challenge, particularly under nonlinear dynamics. We uncover a robust linear relationship between the spectral strength of a node's behavioral time series under evolutionary game dynamics and its structural degree, $S \propto k$, a structural-behavioral scaling that holds across network types and scales, revealing a universal correspondence between local connectivity and dynamic energy. Leveraging this insight, we develop a deterministic, frequency-domain inference framework based on the discrete Fourier transform (DFT) that reconstructs network topology directly from payoff sequences-without prior knowledge of the network or internal node strategies-by selectively perturbing node dynamics. The framework simultaneously localizes individual hidden nodes or identifies all edges connected to multiple hidden nodes, and estimates tight bounds on the number of hidden nodes. Extensive experiments on synthetic and real-world networks demonstrate that our method consistently outperforms state-of-the-art baselines in both topology reconstruction and hidden component detection. Moreover, it scales efficiently to large networks, offering robustness to stochastic fluctuations and overcoming the size limitations of existing techniques. Our work establishes a principled connection between local dynamic observables and global structural inference, enabling accurate topology recovery in complex systems with hidden elements.