  Sensors are commonly deployed to perceive the environment. However, due to
the high cost, sensors are usually sparsely deployed. Kriging is the tailored
task to infer the unobserved nodes (without sensors) using the observed source
nodes (with sensors). The essence of kriging task is transferability. Recently,
several inductive spatio-temporal kriging methods have been proposed based on
graph neural networks, being trained based on a graph built on top of observed
nodes via pretext tasks such as masking nodes out and reconstructing them.
However, the graph in training is inevitably much sparser than the graph in
inference that includes all the observed and unobserved nodes. The learned
pattern cannot be well generalized for inference, denoted as graph gap. To
address this issue, we first present a novel Increment training strategy:
instead of masking nodes (and reconstructing them), we add virtual nodes into
the training graph so as to mitigate the graph gap issue naturally.
Nevertheless, the empty-shell virtual nodes without labels could have
bad-learned features and lack supervision signals. To solve these issues, we
pair each virtual node with its most similar observed node and fuse their
features together; to enhance the supervision signal, we construct reliable
pseudo labels for virtual nodes. As a result, the learned pattern of virtual
nodes could be safely transferred to real unobserved nodes for reliable
kriging. We name our new Kriging model with Increment Training Strategy as
KITS. Extensive experiments demonstrate that KITS consistently outperforms
existing kriging methods by large margins, e.g., the improvement over MAE score
could be as high as 18.33%.
