We present a novel learning-based approach to graph representations of road networks employing state-of-the-art graph convolutional neural networks. Our approach is applied to realistic road networks of 17 cities from Open Street Map. While edge features are crucial to generate descriptive graph representations of road networks, graph convolutional networks usually rely on node features only. We show that the highly representative edge features can still be integrated into such networks by applying a line graph transformation. We also propose a method for neighborhood sampling based on a topological neighborhood composed of both local and global neighbors. We compare the performance of learning representations using different types of neighborhood aggregation functions in transductive and inductive tasks and in supervised and unsupervised learning. Furthermore, we propose a novel aggregation approach, Graph Attention Isomorphism Network, GAIN. Our results show that GAIN outperforms state-of-the-art methods on the road type classification problem.

翻译：我们提出一种新的以学习为基础的方法,用最先进的图形进化神经网络绘制道路网络的图示。我们的方法适用于来自开放街道地图的17个城市的现实道路网络。虽然边缘特征对于产生道路网络的描述性图示至关重要,但图形进化网络通常只依赖节点特征。我们表明,高度代表性的边缘特征仍然可以通过应用线形图转换纳入这种网络。我们还提出了一个基于由当地和全球邻居组成的地形邻居进行社区取样的方法。我们比较了学习表现的表现,在传输和感化任务以及监督和不受监督的学习中,使用不同类型的邻里聚合功能。此外,我们提出了一个新的汇总方法,即“图表注意地貌形态网络”。我们的结果显示,GAIN在道路类型分类问题上优于最先进的方法。