  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.
