Spatio-Temporal Graph Few-Shot Learning with Cross-City Knowledge Transfer
This addresses urban computing challenges in developing cities with limited data, though it appears incremental as it builds on existing cross-city transfer and few-shot learning approaches.
The paper tackles the problem of training spatio-temporal graph models for urban computing tasks in data-scarce cities by proposing ST-GFSL, a model-agnostic few-shot learning framework that transfers knowledge from data-sufficient cities; experiments on four traffic speed prediction benchmarks show it outperforms state-of-the-art methods.
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.