Exdiff: a modular and explainable framework combining network simulation and graph neural networks for diffusion modelling

Abstract Understanding and modelling diffusion processes in complex networks is critical across disciplines, including epidemiology, sociology, and information science. Despite considerable progress, existing approaches often struggle to balance predictive accuracy with interpretability, constraining their applicability in real-world decision-making. ExDiff is introduced as an interactive and modular computational framework that integrates network simulation, Graph Neural Networks (GNNs), and eXplainable Artificial Intelligence (XAI) to both model and elucidate diffusion dynamics. By combining classical compartmental models with deep learning architectures, ExDiff captures the structural and temporal features of diffusion across heterogeneous network topologies. The framework includes modules designed for network analysis, neural modelling, simulation, and interpretability. Its effectiveness is demonstrated through applications to epidemic modelling, including simulation of disease spread, evaluation of intervention strategies, and identification of structural drivers of contagion via XAI techniques. The framework is available in the following GitHub repository: https://github.com/hguzzi/ExDiff.git . Graphical Abstract