Artificial Intelligence for Complex Network:  Potential, Methodology and Application

This tutorial will explore the fascinating domain of empirical network modeling through artificial intelligence (AI) techniques, with applications across social media, web systems, and urban environments. Participants will gain valuable insights into incorporating advanced AI methods—such as graph machine learning, deep reinforcement learning, and generative models—within complex network science. The goal is to provide a comprehensive understanding of how these models can effectively represent, predict, and control empirical networked systems with heterogeneous structures and dynamic processes. The tutorial will emphasize two main areas: first, a novel taxonomy that categorizes six key research problems in complex networks alongside corresponding AI methodologies; second, practical applications of AI-enhanced network modeling tools across diverse domains. These include social networks (en-compassing information propagation, social behavior, collaboration, and competition), urban networks (spanning infrastructure systems and various urban dynamics), and artificial neural networks, which inherently possess complex network structures. By the end of the tutorial, participants will have gained practical knowledge in implementing and adapting AI tools for analyzing, predicting, and managing complex networks in various real-world contexts.