This paper introduces a novel spectral framework for analyzing the spatial dynamics of Smart Specialization (S3) diffusion across Romanian regions. While S3 has become a central pillar of EU cohesion policy, the mechanisms underlying inter-regional innovation contagion remain poorly quantified. We address this gap by integrating Principal Component Analysis (PCA) with eigenvalue–eigenvector decomposition to model economic proximity contagion among 42 counties and over 3,000 localities. A contagion matrix is constructed using population, Gross Value Added (GVA), and Geographic Distance to estimate influence potential and structural centrality within the national innovation ecosystem. Our results uncover non-obvious clusters of innovation diffusion, highlight Bucharest and Cluj-Napoca as central propagation hubs, and demonstrate how structural positioning and connectivity can outweigh raw economic mass in determining territorial impact. The methodology offers a scalable and reproducible approach for identifying regional leaders and followers in smart specialization, with clear implications for multilevel governance and targeted policy design. This work contributes to the operationalization of S3 contagion analysis through unsupervised learning and network-based modeling in transitional economies.