LayoutGKN: Graph Similarity Learning of Floor Plans

• URL: http://arxiv.org/abs/2509.03737v1
• Date: Wed, 03 Sep 2025 21:56:16 GMT
• Title: LayoutGKN: Graph Similarity Learning of Floor Plans
• Authors: Casper van Engelenburg, Jan van Gemert, Seyran Khademi,
• Abstract summary: Most successful methods to compare graphs rely on costly intermediate cross-graph node-level interactions.<n>We introduce textbfGKN, a more efficient approach that postpones the cross-graph node-level interactions.<n>We show that LayoutGKN computes similarity comparably or better than graph matching networks.
• Score: 11.388505978767627
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 \textbf{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. \href{https://github.com/caspervanengelenburg/LayoutGKN}{Code and data} are open.

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