  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.
