  Graph neural networks (GNNs) have been regarded as the basic model to
facilitate deep learning (DL) to revolutionize resource allocation in wireless
networks. GNN-based models are shown to be able to learn the structural
information about graphs representing the wireless networks to adapt to the
time-varying channel state information and dynamics of network topology. This
article aims to provide a comprehensive overview of applying GNNs to optimize
wireless networks via answering three fundamental questions, i.e., how to input
the wireless network data into GNNs, how to improve the performance of GNNs,
and how to evaluate GNNs. Particularly, two graph representations are given to
transform wireless network parameters into graph-structured data. Then, we
focus on the architecture design of the GNN-based models via introducing the
basic message passing as well as model improvement methods including multi-head
attention mechanism and residual structure. At last, we give task-oriented
evaluation metrics for DL-enabled wireless resource allocation. We also
highlight certain challenges and potential research directions for the
application of GNNs in wireless networks.
