  Wireless networks are inherently graph-structured, which can be utilized in
graph representation learning to solve complex wireless network optimization
problems. In graph representation learning, feature vectors for each entity in
the network are calculated such that they capture spatial and temporal
dependencies in their local and global neighbourhoods. Graph neural networks
(GNNs) are powerful tools to solve these complex problems because of their
expressive representation and reasoning power. In this paper, the potential of
graph representation learning and GNNs in wireless networks is presented. An
overview of graph learning is provided which covers the fundamentals and
concepts such as feature design over graphs, GNNs, and their design principles.
Potential of graph representation learning in wireless networks is presented
via few exemplary use cases and some initial results on the GNN-based access
point selection for cell-free massive MIMO systems.
