Graph Representation Learning for Road Type Classification

TL;DR

This paper introduces GAIN, a novel graph neural network architecture that effectively incorporates edge features via line graph transformation, improving road type classification accuracy on real-world datasets.

Contribution

The paper proposes GAIN, a new graph attention network that integrates edge features through line graph transformation and neighborhood sampling, advancing road network classification methods.

Findings

GAIN outperforms existing methods on road type classification

Edge features significantly improve graph representations

The proposed neighborhood sampling enhances learning performance

Abstract

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.