  Present-day graphical user interfaces (GUIs) exhibit diverse arrangements of
text, graphics, and interactive elements such as buttons and menus, but
representations of GUIs have not kept up. They do not encapsulate both semantic
and visuo-spatial relationships among elements. To seize machine learning's
potential for GUIs more efficiently, Graph4GUI exploits graph neural networks
to capture individual elements' properties and their semantic-visuo-spatial
constraints in a layout. The learned representation demonstrated its
effectiveness in multiple tasks, especially generating designs in a challenging
GUI autocompletion task, which involved predicting the positions of remaining
unplaced elements in a partially completed GUI. The new model's suggestions
showed alignment and visual appeal superior to the baseline method and received
higher subjective ratings for preference. Furthermore, we demonstrate the
practical benefits and efficiency advantages designers perceive when utilizing
our model as an autocompletion plug-in.
