The technology is an innovative Web-App integrating Network Science and Explainable AI (XAI) to model, analyze, and transfer knowledge across heterogeneous scientific domains (e.g., neuroscience, materials, social systems, astrophysics). The system converts complex data into a common abstract framework: Network Science maps dynamic relationships, while XAI identifies relevant features, ensuring process transparency. A key strength is transfer learning, enabling the application of models learned in data-rich sectors to data-scarce contexts, thereby reducing time and resources. For instance, an algorithm trained on vast human neural networks can be readapted to study galaxy distribution or the resilience of new materials, where data is limited. The solution supports interdisciplinary research, the discovery of universal patterns, and the development of advanced applications in different domains (e.g., biomedicine, smart materials, economy, astrophysics, social science, etc.).
The innovation lies in the unique integration of Network Science and XAI into a framework that makes different systems comparable (from the brain to galaxies). Compared to current vertical, domain-specific solutions, this platform is transversal: it allows identifying common structural principles and transferring predictive models from "data-rich" to "data-poor" sectors. This transfer learning approach drastically reduces costs and training times. Furthermore, the use of XAI ensures the transparency ("white-box") of results, overcoming the opacity of traditional "black-box" models and facilitating scientific and industrial validation. The web-app offers a scalable tool for research institutions and the high-tech industry.