Transferable Graph Neural Networks for Inferring Road Type Attributes in Street Networks

Abstract

In this paper, we study transferable graph neural networks for street networks. The use of Graph Neural Networks in a transfer learning setting is a promising approach to overcome issues such as the lack of good quality data for training purposes. With transfer learning, we can fine-tune a model trained on a rich sample of data before applying to a task with limited observations. Specifically, we focus on the open research problem of inferring the attributes of a street network as a node classification task. An attribute contains descriptive information about a street segment such as the street type. We propose and develop a neural framework capable of learning from multiple street networks using transfer learning to infer the semantics of another street network. Different from previous studies, we are the first to address this problem by learning from more than one street network using graph neural networks. We empirically evaluate our framework on multiple large real-world networks. Our evaluations show that while state-of-the-art methods can be negatively impacted by naive transfer learning, our framework consistently mitigates this phenomenon, with up to a 10% gain in mean transfer accuracy.