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, compared to conventional methods like 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 the applications of graphs in wireless networks. Then, we introduce and analyse common GAI models from the perspective of graph generation. Moreover, we propose a framework that incorporates a conditional diffusion model and an evaluation network, which can be trained with reward functions and conditions customized by the user. 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 sensing link selection in an integrated sensing and communication as an example, the effectiveness of the proposed framework is validated.
