使用GNN和图核量化全球城市道路网络的非异态性
Linfang Tian1, Weixiong Rao2, Kai Zhao3
1School of Software Engineering, Tongji University, Shanghai, 201804, China. tianlinfang@tongji.edu.cn.
Scientific reports
|February 22, 2025
概括
这项研究引入了一种新的方法,用于使用图形分类准确度量化图形非异态. 边缘卷积神经网络 (EdgeCNN) 在区分城市道路网络方面表现优于传统的图核.
科学领域:
- 图形理论 图形理论
- 网络分析 网络分析
- 机器学习 机器学习
背景情况:
- 传统的图形异态性测试在测量结构差异方面存在严格的限制.
- 量化图形非同态性对于分析城市基础设施等复杂网络至关重要.
研究的目的:
- 引入一种用于量化图形非异态的新概念.
- 评估图形神经网络 (GNN) 和图形内核,以对城市道路网络进行分类.
- 建议将图形分类准确性作为图形非异态度的度量.
主要方法:
- 训练图形神经网络 (GNN),特别是边缘卷积神经网络 (EdgeCNN) 和图形内核.
- 从全球30个城市分类了10,361个城市道路网络.
- 将EdgeCNN的分类精度与韦斯费勒-莱曼 (WL) 内核算法进行比较.
主要成果:
- 边缘CNN实现了85%的分类准确度,有效地使用了节点和边缘功能.
- 边缘CNN超过了Weisfeiler-Lehman (WL) 内核算法的准确率80%.
- 这些结果挑战了GNN受限于WL测试在区分图形结构方面的能力的概念.
结论:
- 图形分类准确性可以作为量化图形非同态性的可行度量.
- 边缘CNN在分析城市道路网络的结构差异方面表现出卓越的表现.
- 这些发现为城市发展和网络分析提供了宝贵的见解.
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