Graph4GUI: Graph Neural Networks for Representing Graphical User Interfaces

arXiv:2404.13521v1 Announce Type: cross
Abstract: 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 …

abstract arxiv cs.ai cs.cv cs.hc cs.lg diverse graph graphics graph neural networks interactive interfaces machine machine learning networks neural networks relationships semantic spatial text type