Spatio-Temporal Graph Few-Shot Learning with Cross-City Knowledge Transfer

TL;DR

This paper introduces ST-GFSL, a novel model-agnostic few-shot learning framework for spatio-temporal graphs that transfers cross-city knowledge to improve urban forecasting tasks with limited data.

Contribution

The paper proposes a new few-shot learning method that generates non-shared parameters and reconstructs graph structures to effectively transfer knowledge across cities.

Findings

ST-GFSL outperforms state-of-the-art methods on traffic speed prediction benchmarks.

The approach effectively handles irregular graph structures across different cities.

Experimental results validate the robustness of the proposed framework.

Abstract

Spatio-temporal graph learning is a key method for urban computing tasks, such as traffic flow, taxi demand and air quality forecasting. Due to the high cost of data collection, some developing cities have few available data, which makes it infeasible to train a well-performed model. To address this challenge, cross-city knowledge transfer has shown its promise, where the model learned from data-sufficient cities is leveraged to benefit the learning process of data-scarce cities. However, the spatio-temporal graphs among different cities show irregular structures and varied features, which limits the feasibility of existing Few-Shot Learning (\emph{FSL}) methods. Therefore, we propose a model-agnostic few-shot learning framework for spatio-temporal graph called ST-GFSL. Specifically, to enhance feature extraction by transfering cross-city knowledge, ST-GFSL proposes to generate non-shared parameters based on node-level meta knowledge. The nodes in target city transfer the knowledge via parameter matching, retrieving from similar spatio-temporal characteristics. Furthermore, we propose to reconstruct the graph structure during meta-learning. The graph reconstruction loss is defined to guide structure-aware learning, avoiding structure deviation among different datasets. We conduct comprehensive experiments on four traffic speed prediction benchmarks and the results demonstrate the effectiveness of ST-GFSL compared with state-of-the-art methods.