The episode describes GRAF, an innovative framework designed for the analysis of heterogeneous and multiplex networks. GRAF simplifies these complex networks by transforming them into homogeneous networks, leveraging attention mechanisms to assess the relative importance of nodes and relationships. This approach enhances the performance of machine learning models, delivering more accurate and interpretable results. Applications in various fields, such as bioinformatics and bibliometric data analysis, are presented, highlighting GRAF’s robustness and generalization capabilities. Finally, the discussion explores potential future implications and the prospective impact of GRAF across different industries.