Artificial Intelligence for Complex Network: Potential, Methodology and Application

Abstract

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 begin by introducing essential background knowledge, outlining motivations and challenges, exploring recent methodological advances, and highlighting key applications.