用图核加速平面布局相似性计算，速度远超传统方法
LayoutGKN: Graph Similarity Learning of Floor Plans
- 将跨图节点交互延迟到嵌入末尾，减少计算开销
- 在相同精度下，推理速度提升显著（具体倍数未提）
- 适合需要快速匹配建筑平面图的场景
平面图描绘建筑布局，常以图结构表示以捕捉空间关系。图比较对搜索、聚类和数据可视化等应用至关重要。现有最成功的方法（如图匹配网络）依赖代价高昂的跨图节点级交互，导致推理缓慢。本文提出布局图核网络（LayoutGKN），将跨图节点级交互推迟至联合嵌入架构末端，利用可微图核作为最终节点嵌入的距离函数。实验表明，LayoutGKN在相似性计算性能上与图匹配网络相当或更优，同时显著提升推理速度。代码与数据已开源。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。