Networked data science: a unified network modeling framework

Abstract This work introduces a data-centric framework for answering analytical questions using network models, transcending domain-specific modeling conventions. The unified network modeling framework (UNMF) provides an interdisciplinary strategy for principled modeling and analysis of complex networks, including multilayer and temporal networks that arise in sustainability-driven applications. UNMF connects dynamic analysis, pattern discovery, and network-grounded integration of heterogeneous sources (structured and unstructured). We present guided instantiations of UNMF in urban development, mobility, ecosystems, and social-network settings to show how explicit modeling choices can be documented, compared, and assessed within a shared evaluative framework. In doing so, we formalize and systematize Networked Data Science as a field at the intersection of network science and data science, and we define its scope and applications. This work contributes to network science by providing an auditable design-and-evaluation procedure for studying complex, evolving systems and for making representation choices more explicit, inspectable, and reusable across domains.