基于图神经网络的复杂系统建模与仿真

doi:10.16182/j.issn1004731x.joss.25-0512

系统仿真学报 ›› 2025, Vol. 37 ›› Issue (7): 1624-1638.doi: 10.16182/j.issn1004731x.joss.25-0512

• 特约综述 • 上一篇

基于图神经网络的复杂系统建模与仿真

吕金虎¹^,², 蒋弘毅²^,³, 刘德元¹^,², 谭少林²

^1.北京航空航天大学自动化科学与电气工程学院，北京 100191
^2.中关村实验室，北京 100094
^3.北京航空航天大学人工智能学院，北京 100191

收稿日期:2025-06-04 修回日期:2025-06-13 出版日期:2025-07-18 发布日期:2025-07-30
通讯作者: 蒋弘毅
第一作者简介:吕金虎 1974年生，北京航空航天大学蓝天讲席教授，副校长，国家级领军人才、基金委创新群体学术带头人，IEEE/CAA/ORSC/CICC/CSIAM Fellow。全国科技创新领军人才联盟理事长、中国指挥与控制学会/中国仿真学会副理事长、中国自动化学会常务理事。长期从事复杂系统协同控制、工业互联网、集群智能等研究，主持国家工业互联网重大项目、国家重点研发计划项目等，WoS总他引2.46万余次，授权发明专利191项，十次入选年度全球高被引科学家，曾任IEEE TII共同主编。曾获3项国家自然科学二等奖、5项省部级科技一等奖、何梁何利科技进步奖、国家级教学成果二等奖、中国工程院光华青年奖、中国科学院青年科学家奖、中国青年科技奖、中国航天基金会钱学森杰出贡献奖等。
吕金虎(1974-)，男，教授，博士，研究方向为复杂系统控制、集群智能。
基金资助:
国家自然科学基金(T2322023)

Modeling and Simulation of Complex Systems Based on Graph Neural Networks

Lü Jinhu¹^,², Jiang Hongyi²^,³, Liu Deyuan¹^,², Tan Shaolin²

^1.School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China
^2.Zhongguancun Laboratory, Beijing 100094, China
^3.School of Artificial Intelligence, Beihang University, Beijing 100191, China

Received:2025-06-04 Revised:2025-06-13 Online:2025-07-18 Published:2025-07-30
Contact: Jiang Hongyi

摘要/Abstract

摘要：

如何对复杂系统建模与仿真是理解其结构和功能的关键问题。图神经网络学习和表征数据内部关联关系的能力，为复杂系统建模与仿真提供了新的方法。当前，存在包括频域、空域、生成式、异构、时空等多种类型的图神经网络模型，并根据特定任务和场景，广泛应用于工业互联网、社会网络、供应链等多个领域中的复杂系统建模与仿真研究中。从网络拓扑表征、动态演化建模、系统行为与结构生成等3个典型任务出发，阐述图神经网络在复杂系统建模与仿真中的应用，分析图神经网络在捕捉复杂系统多层次结构、动态交互和非线性关系的优势，为复杂系统与图神经网络交叉融合研究提供新的参考。

关键词: 复杂系统, 图神经网络, 结构表征, 复杂网络, 图表示学习

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.

Key words: complex system, graph neural networks(GNNs), structural representation, complex network, graph representation learning

中图分类号:

TP183

吕金虎,蒋弘毅,刘德元等 . 基于图神经网络的复杂系统建模与仿真[J]. 系统仿真学报, 2025, 37(7): 1624-1638.

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.

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维度	现有建模与仿真方法	基于图神经网络的建模与仿真方法
理论基础	微分方程、演化动力学等数学物理模型	图论、深度学习等统计机器学习模型
建模对象	线性系统、欧氏空间下的规则网格数据等	非欧、动态、异构的复杂数据
仿真计算效率	需进行大量精确运算，效率较低	训练成本较高，但在推理阶段速度较快
动态适应性	需针对具体对象调整模型及其参数	在性质相似的数据上具有较强泛化性
可解释性	物理意义明确	仍为开放的研究问题

类型	代表性方法
消息传递网络(MPNNs)	GCN^[16,42]、ChebNet^[15]、DCNN^[43]、GAT^[12]、GATv2^[13]、GIN^[18]、GraphSAGE^[44]等
Graph Transformer	GraphGPS^[31]、SAT^[32]、Exphormer^[33]、Graphormer^[34]、Polynormer^[35]、Graph Mamba^[36]、HDSE^[45]、GPSE^[46]等
基于随机游走的图模型	CRaWl^[47]、RAW-GNN^[48]、RWC^[49]、NeuralWalker^[50]等
基于热扩散的图模型	HiD-Net^[51]、GDC^[52]、ADC^[53]、GRAND^[54]、NDCN^[55]、NIGCN^[56]等

基于图神经网络的复杂系统建模与仿真

Modeling and Simulation of Complex Systems Based on Graph Neural Networks

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