  Despite the suitability of graphs for capturing the relational structures
inherent in architectural layout designs, there is a notable dearth of research
on interpreting architectural design space using graph-based representation
learning and exploring architectural design graph generation. Concurrently,
disentangled representation learning in graph generation faces challenges such
as node permutation invariance and representation expressiveness. To address
these challenges, we introduce an unsupervised disentangled representation
learning framework, Style-based Edge-augmented Variational Graph Auto-Encoder
(SE-VGAE), aiming to generate architectural layout in the form of attributed
adjacency multi-graphs while prioritizing representation disentanglement. The
framework is designed with three alternative pipelines, each integrating a
transformer-based edge-augmented encoder, a latent space disentanglement
module, and a style-based decoder. These components collectively facilitate the
decomposition of latent factors influencing architectural layout graph
generation, enhancing generation fidelity and diversity. We also provide
insights into optimizing the framework by systematically exploring graph
feature augmentation schemes and evaluating their effectiveness for
disentangling architectural layout representation through extensive
experiments. Additionally, we contribute a new benchmark large-scale
architectural layout graph dataset extracted from real-world floor plan images
to facilitate the exploration of graph data-based architectural design
representation space interpretation. This study pioneered disentangled
representation learning for the architectural layout graph generation. The code
and dataset of this study will be open-sourced.
