# LayoutGKN: Graph Similarity Learning of Floor Plans

arXiv: https://arxiv.org/abs/2509.03737 · HTML: https://mlsanity.com/paper/2509.03737/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 40 references that are in the map.
- First posted on arXiv 3 Sep 2025.

## Builds on (most cited first, 15 of 15)

- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018)
- [Deep metric learning using Triplet network](https://mlsanity.com/paper/1412.6622/) (2014)
- [Graph Matching Networks for Learning the Similarity of Graph Structured Objects](https://mlsanity.com/paper/1904.12787/) (2019)
- [A Survey on Graph Kernels](https://mlsanity.com/paper/1903.11835/) (2019)
- [SimGNN: A Neural Network Approach to Fast Graph Similarity Computation](https://mlsanity.com/paper/1808.05689/) (2018)
- [House-GAN: Relational Generative Adversarial Networks for Graph-constrained House Layout Generation](https://mlsanity.com/paper/2003.06988/) (2020)
- [Segment Anything Model for Road Network Graph Extraction](https://mlsanity.com/paper/2403.16051/) (2024)
- [Deep Graph Similarity Learning: A Survey](https://mlsanity.com/paper/1912.11615/) (2019)
- [LayoutGMN: Neural Graph Matching for Structural Layout Similarity](https://mlsanity.com/paper/2012.06547/) (2020)
- [MSD: A Benchmark Dataset for Floor Plan Generation of Building Complexes](https://mlsanity.com/paper/2407.10121/) (2024)
- [COHO: Context-Sensitive City-Scale Hierarchical Urban Layout Generation](https://mlsanity.com/paper/2407.11294/) (2024)
- [Learning Graph Edit Distance by Graph Neural Networks](https://mlsanity.com/paper/2008.07641/) (2020)
- [SSIG: A Visually-Guided Graph Edit Distance for Floor Plan Similarity](https://mlsanity.com/paper/2309.04357/) (2023)

## Built on by (most cited first, 2 of 2)

- [HypergraphFormer: Learning Hypergraphs from LLMs for Editable Floor Plan Generation](https://mlsanity.com/paper/2605.18932/) (2026)
- [Towards an automated AI-based framework for floor plan compliance checks for residential buildings](https://mlsanity.com/paper/2607.00015/) (2026)

## Abstract (arXiv)

Floor plans depict building layouts and are often represented as graphs to capture the underlying spatial relationships. Comparison of these graphs is critical for applications like search, clustering, and data visualization. The most successful methods to compare graphs ie, graph matching networks, rely on costly intermediate cross-graph node-level interactions, therefore being slow in inference time. We introduce LayoutGKN, a more efficient approach that postpones the cross-graph node-level interactions to the end of the joint embedding architecture. We do so by using a differentiable graph kernel as a distance function on the final learned node-level embeddings. We show that LayoutGKN computes similarity comparably or better than graph matching networks while significantly increasing the speed. hrefhttps://github.com/caspervanengelenburg/LayoutGKNCode and data are open.