ExDiff: A Framework for Simulating Diffusion Processes on Complex Networks with Explainable AI Integration

TL;DR

ExDiff is a comprehensive framework that combines network simulation, graph neural networks, and explainable AI to model, interpret, and analyze diffusion processes in complex networks across various domains.

Contribution

It introduces a modular platform integrating classical models with deep learning and XAI, enabling simulation and interpretability of diffusion dynamics in diverse network topologies.

Findings

Successfully simulates disease spread using SIRVD model

Evaluates intervention strategies effectively

Reveals structural determinants of contagion through XAI

Abstract

Understanding and controlling diffusion processes in complex networks is critical across domains ranging from epidemiology to information science. Here, we present ExDiff, an interactive and modular computational framework that integrates network simulation, graph neural networks (GNNs), and explainable artificial intelligence (XAI) to model and interpret diffusion dynamics. ExDiff combines classical compartmental models with deep learning techniques to capture both the structural and temporal characteristics of diffusion across diverse network topologies. The framework features dedicated modules for network analysis, neural modeling, simulation, and interpretability, all accessible via an intuitive interface built on Google Colab. Through a case study of the Susceptible Infectious Recovered Vaccinated Dead (SIRVD) model, we demonstrate the capacity to simulate disease spread, evaluate intervention strategies, classify node states, and reveal the structural determinants of contagion through XAI techniques. By unifying simulation and interpretability, ExDiff provides a powerful, flexible, and accessible platform for studying diffusion phenomena in networked systems, enabling both methodological innovation and practical insight.