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

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.