Int J Performability Eng ›› 2022, Vol. 18 ›› Issue (4): 241-250.doi: 10.23940/ijpe.22.04.p2.241250

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A Survey of the Analysis of Complex Systems based on Complex Network Theory and Deep Learning

Dan Lu and Shunkun Yang*   

  1. School of Reliability and Systems Engineering, Beihang University, Beijing, 100191, China
  • Submitted on ; Revised on ; Accepted on
  • Contact: * E-mail address:

Abstract: From the perspective of complex network theory, complex systems can be characterized by the interaction of microscopic units through nonlinear effects, yielding macroscopic emergent behavior. In light of the powerful capability of deep learning in feature extraction and model fitting from large amount of datasets, we try to overview the benefits of combining the complex network analysis with deep learning techniques to investigate complex systems. We first explore the existence of complexity in complex systems. In what followed, we first give a brief description of complex network theory. Then, we present an overview of deep learning technology. Subsequently, we focus on the research advances and applications in the analysis of complex systems based on complex network theory and deep learning. The last section is further discussion and prospects for the combination of these two methods. In a nutshell, the development of deep learning combined with complex network theory allows for exploring the complexity in complex systems at a higher level.

Key words: complexity, complex systems, complex networks, deep learning