Modeling and Simulation of Complex Systems Based on Graph Neural Networks

Authors

Jinhu Lü, School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China; Zhongguancun Laboratory, Beijing 100094,China
Hongyi Jiang, Zhongguancun Laboratory, Beijing 100094,China; School of Artificial Intelligence, Beihang University, Beijing 100191, China
Deyuan Liu, School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China; Zhongguancun Laboratory, Beijing 100094,China
Shaolin Tan, Zhongguancun Laboratory, Beijing 100094,China

Abstract

Abstract: Modeling and simulation of complex systems are critical issues for understanding their structural and functional properties. The ability of graph neural networks (GNNs) to learn and represent the internal correlations within data provides a new approach for modeling and simulating complex systems. Currently, there are various types of GNN models involving frequency-domain, spatial-domain, generative, heterogeneous, and spatio-temporal models. These models are widely applied in complex system modeling and simulation research in multiple fields such as industrial internet, social networks, and supply chains based on specific tasks and scenarios. Starting from three representative tasks: network topology representation, dynamic evolution modeling, and system behavior and structure generation, this paper systematically analyzed GNN applications in modeling and simulation of complex systems, and its advantages in capturing multi-level structures, dynamic interactions, and nonlinear relationships within complex systems were explored. It provided a new reference for the research on the integration of complex systems with GNNs.

Recommended Citation

Lü, Jinhu; Jiang, Hongyi; Liu, Deyuan; and Tan, Shaolin (2025) "Modeling and Simulation of Complex Systems Based on Graph Neural Networks," Journal of System Simulation: Vol. 37: Iss. 7, Article 3.
DOI: 10.16182/j.issn1004731x.joss.25-0512
Available at: https://dc-china-simulation.researchcommons.org/journal/vol37/iss7/3

First Page

1624

Last Page

1638

CLC

TP183

Recommended Citation

Lü Jinhu, Jiang Hongyi, Liu Deyuan, et al. Modeling and Simulation of Complex Systems Based on Graph Neural Networks[J]. Journal of System Simulation, 2025, 37(7): 1624-1638.

Corresponding Author

Jiang Hongyi

DOI

10.16182/j.issn1004731x.joss.25-0512