Research on a Method for Generating 3D Topologies of Combat Networks Based on Conditional Graph Diffusion Models

Yang, Xiaofei; Yang, Wenjing; Gao, Mei; He, Bo; Wang, Xiaoshuang; Lin, Zhiqiang

doi:10.3390/sym18010184

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Research on a Method for Generating 3D Topologies of Combat Networks Based on Conditional Graph Diffusion Models

by

Xiaofei Yang

^1,2

,

Wenjing Yang

^1,2,*,

Mei Gao

^1,2,

Bo He

^1,2,

Xiaoshuang Wang

^1,2 and

Zhiqiang Lin

²

¹

College of Information and Communication, National University of Defense Technology, Changsha 410073, China

²

Information Support Force Engineering University, Wuhan 430010, China

^*

Author to whom correspondence should be addressed.

Symmetry 2026, 18(1), 184; https://doi.org/10.3390/sym18010184

Submission received: 3 December 2025 / Revised: 10 January 2026 / Accepted: 15 January 2026 / Published: 19 January 2026

(This article belongs to the Section Computer)

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Abstract

This paper addresses the pressing need for the intelligent design of three-dimensional topological structures in combat networks within modern joint operations. Conventional graph generation approaches struggle to simultaneously fulfill requirements for 3D deployment, tactical effectiveness, and real-time generation in complex battlefield environments. To overcome these challenges, we propose a method for generating 3D combat network topologies using a conditional graph diffusion model. Our primary innovation lies in a conditional diffusion framework guided by the fusion of target attributes. Through a multi-dimensional conditional embedding mechanism, we integrate combat node types, equipment characteristics, 3D spatial constraints, and tactical requirements into a unified generation process. This enables the model to generate topologies that deeply integrate operational rules and tactical demands. Experimental results demonstrate that our approach significantly improves core tactical metrics: target accessibility increases by 4.5%, defensive capability improves by 13.15%, and offensive efficiency rises by 30.4%. The results indicate that the proposed method achieves superior adaptability and robustness in complex battlefield environments.

Keywords: combat networks; generation of 3D topology; conditional graph diffusion; generative methods

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MDPI and ACS Style

Yang, X.; Yang, W.; Gao, M.; He, B.; Wang, X.; Lin, Z. Research on a Method for Generating 3D Topologies of Combat Networks Based on Conditional Graph Diffusion Models. Symmetry 2026, 18, 184. https://doi.org/10.3390/sym18010184

AMA Style

Yang X, Yang W, Gao M, He B, Wang X, Lin Z. Research on a Method for Generating 3D Topologies of Combat Networks Based on Conditional Graph Diffusion Models. Symmetry. 2026; 18(1):184. https://doi.org/10.3390/sym18010184

Chicago/Turabian Style

Yang, Xiaofei, Wenjing Yang, Mei Gao, Bo He, Xiaoshuang Wang, and Zhiqiang Lin. 2026. "Research on a Method for Generating 3D Topologies of Combat Networks Based on Conditional Graph Diffusion Models" Symmetry 18, no. 1: 184. https://doi.org/10.3390/sym18010184

APA Style

Yang, X., Yang, W., Gao, M., He, B., Wang, X., & Lin, Z. (2026). Research on a Method for Generating 3D Topologies of Combat Networks Based on Conditional Graph Diffusion Models. Symmetry, 18(1), 184. https://doi.org/10.3390/sym18010184

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Research on a Method for Generating 3D Topologies of Combat Networks Based on Conditional Graph Diffusion Models

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