# VLM-based automatic multi-granularity graph representation of building layouts for design informatics

arXiv: https://arxiv.org/abs/2608.24886 · HTML: https://mlsanity.com/paper/2608.24886/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 70 references that are in the map.
- First posted on arXiv 8 May 2026.

## Builds on (most cited first, 14 of 14)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018)
- [Strategies for Pre-training Graph Neural Networks](https://mlsanity.com/paper/1905.12265/) (2019)
- [SpatialVLM: Endowing Vision-Language Models with Spatial Reasoning Capabilities](https://mlsanity.com/paper/2401.12168/) (2024)
- [Do Vision Transformers See Like Convolutional Neural Networks?](https://mlsanity.com/paper/2108.08810/) (2021)
- [SpatialBot: Precise Spatial Understanding with Vision Language Models](https://mlsanity.com/paper/2406.13642/) (2024)
- [HouseDiffusion: Vector Floorplan Generation via a Diffusion Model with Discrete and Continuous Denoising](https://mlsanity.com/paper/2211.13287/) (2022)
- [Vision Language Models Can Parse Floor Plan Maps](https://mlsanity.com/paper/2409.12842/) (2024)
- [Graph-Based Generative Representation Learning of Semantically and Behaviorally Augmented Floorplans](https://mlsanity.com/paper/2012.04735/) (2020)
- [A hypergraph model shows the carbon reduction potential of effective space use in housing](https://mlsanity.com/paper/2405.01290/) (2024)
- [HouseTune: Two-Stage Floorplan Generation with LLM Assistance](https://mlsanity.com/paper/2411.12279/) (2024)
- [GRAIN: Multi-Granular and Implicit Information Aggregation Graph Neural Network for Heterophilous Graphs](https://mlsanity.com/paper/2504.06649/) (2025)
- [Representing Big Data as Networks: New Methods and Insights](https://mlsanity.com/paper/1712.09648/) (2017)

## Abstract (arXiv)

Architectural floorplan images encode rich relational knowledge among functional spaces, which underpins design retrieval, knowledge-based reasoning, and BIM enrichment through the building lifecycle. However, it remains challenging to automatically construct task-adaptive graph representations for public buildings. To address this gap, we first define a multi-granularity Level-of-Graphs (LoGs) for public building layouts. Methodologically, we present a Vision-Language Model (VLM)-based automatic LoG construction through node identification, edge inference, text parsing, and graph coarsening. VLM-generated representations are systematically evaluated and tested in real-world tasks, using 147 academic library floorplans worldwide as a case study. Experiments showed VLM-generated graphs were broadly consistent with human-labeled graphs (matched node ratio >= 92%; 509.3 s per floor plan for three-LoG graph generation). Meso-grained graphs yield the best node-level zone prediction (Macro F1 = 0.647, at 65% of fine-grained complexity), while coarse-grained graphs are most effective for graph-level layout quality evaluation (Spearman's rho = 0.610, at 16% of fine-grained complexity). By enabling scalable, annotation-free extraction of structured layout information from floorplan images, this study advances design informatics by converting plan images into knowledge representations, thereby enhancing the utilization of design information across the building life cycle.