  The analysis of building models for usable area, building safety, and energy
use requires accurate classification data of spaces and space elements. To
reduce input model preparation effort and errors, automated classification of
spaces and space elements is desirable. A barrier hindering the utilization of
Graph Deep Learning (GDL) methods to space function and space element
classification is a lack of suitable datasets. To bridge this gap, we introduce
a dataset, SAGC-A68, which comprises access graphs automatically generated from
68 digital 3D models of space layouts of apartment buildings. This graph-based
dataset is well-suited for developing GDL models for space function and space
element classification. To demonstrate the potential of the dataset, we employ
it to train and evaluate a graph attention network (GAT) that predicts 22 space
function and 6 space element classes. The dataset and code used in the
experiment are available online. https://doi.org/10.5281/zenodo.7805872,
https://github.com/A2Amir/SAGC-A68.
