  Due to mutual interference between users, power allocation problems in
wireless networks are often non-convex and computationally challenging. Graph
neural networks (GNNs) have recently emerged as a promising approach to
tackling these problems and an approach that exploits the underlying topology
of wireless networks. In this paper, we propose a novel graph representation
method for wireless networks that include full-duplex (FD) nodes. We then
design a corresponding FD Graph Neural Network (F-GNN) with the aim of
allocating transmit powers to maximise the network throughput. Our results show
that our F-GNN achieves state-of-art performance with significantly less
computation time. Besides, F-GNN offers an excellent trade-off between
performance and complexity compared to classical approaches. We further refine
this trade-off by introducing a distance-based threshold for inclusion or
exclusion of edges in the network. We show that an appropriately chosen
threshold reduces required training time by roughly 20% with a relatively minor
loss in performance.
