In wireless communications, transforming network into graphs and processing them using deep learning models, such as Graph Neural Networks (GNNs), is one of the mainstream network optimization approaches. While effective, the generative AI (GAI) shows stronger capabilities in graph analysis, processing, and generation, than conventional methods such as GNN, offering a broader exploration space for graph-based network optimization. Therefore, this article proposes to use GAI-based graph generation to support wireless networks. Specifically, we first explore applications of graphs in wireless networks. Then, we introduce and analyze common GAI models from the perspective of graph generation. On this basis, we propose a framework that incorporates the conditional diffusion model and an evaluation network, which can be trained with reward functions and conditions customized by network designers and users. Once trained, the proposed framework can create graphs based on new conditions, helping to tackle problems specified by the user in wireless networks. Finally, using the link selection in integrated sensing and communication (ISAC) as an example, the effectiveness of the proposed framework is validated.

翻译：在无线通信中，将网络转化为图并通过图神经网络（GNN）等深度学习模型进行处理，是主流的网络优化方法之一。尽管GNN等传统方法有效，但生成式人工智能（GAI）在图分析、处理和生成方面展现出更强的能力，为基于图的网络优化提供了更广阔的探索空间。为此，本文提出利用基于GAI的图生成来支持无线网络。具体而言，我们首先探讨了图在无线网络中的应用场景，随后从图生成角度介绍并分析了常见的GAI模型。在此基础上，我们提出了一种融合条件扩散模型与评估网络的框架，该框架可通过网络设计者与用户自定义的奖励函数及条件进行训练。训练完成后，所提框架能根据新条件生成图，助力解决用户在无线网络中指定的问题。最后，以集成感知与通信（ISAC）中的链路选择为例，验证了所提框架的有效性。