Graph4GUI: Graph Neural Networks for Representing Graphical User Interfaces
This work addresses the challenge of efficient GUI design for designers by providing an incremental improvement in representation methods for autocompletion tools.
The paper tackled the problem of representing graphical user interfaces (GUIs) by developing Graph4GUI, a graph neural network model that captures semantic and visuo-spatial relationships among elements, resulting in superior performance in GUI autocompletion tasks with higher subjective ratings for preference compared to baselines.
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.