Artificial Intelligence for Complex Network: Potential, Methodology and
  Application
Jingtao Ding Chang Liu Yu Zheng Yunke Zhang Zihan Yu Ruikun Li Hongyi Chen Jinghua Piao Huandong Wang Jiazhen Liu Yong Li
Complex networks pervade various real-world systems, from the natural
environment to human societies. The essence of these networks is in their
ability to transition and evolve from microscopic disorder-where network
topology and node dynamics intertwine-to a macroscopic order characterized by
certain collective behaviors. Over the past two decades, complex network
science has significantly enhanced our understanding of the statistical
mechanics, structures, and dynamics underlying real-world networks. Despite
these advancements, there remain considerable challenges in exploring more
realistic systems and enhancing practical applications. The emergence of
artificial intelligence (AI) technologies, coupled with the abundance of
diverse real-world network data, has heralded a new era in complex network
science research. This survey aims to systematically address the potential
advantages of AI in overcoming the lingering challenges of complex network
research. It endeavors to summarize the pivotal research problems and provide
an exhaustive review of the corresponding methodologies and applications.
Through this comprehensive survey-the first of its kind on AI for complex
networks-we expect to provide valuable insights that will drive further
research and advancement in this interdisciplinary field.