Non-Neighbors Also Matter to Kriging: A New Contrastive-Prototypical
  Learning
Zhishuai Li Yunhao Nie Ziyue Li Lei Bai Yisheng Lv Rui Zhao
Kriging aims at estimating the attributes of unsampled geo-locations from
observations in the spatial vicinity or physical connections, which helps
mitigate skewed monitoring caused by under-deployed sensors. Existing works
assume that neighbors' information offers the basis for estimating the
attributes of the unobserved target while ignoring non-neighbors. However,
non-neighbors could also offer constructive information, and neighbors could
also be misleading. To this end, we propose ``Contrastive-Prototypical''
self-supervised learning for Kriging (KCP) to refine valuable information from
neighbors and recycle the one from non-neighbors. As a pre-trained paradigm, we
conduct the Kriging task from a new perspective of representation: we aim to
first learn robust and general representations and then recover attributes from
representations. A neighboring contrastive module is designed that coarsely
learns the representations by narrowing the representation distance between the
target and its neighbors while pushing away the non-neighbors. In parallel, a
prototypical module is introduced to identify similar representations via
exchanged prediction, thus refining the misleading neighbors and recycling the
useful non-neighbors from the neighboring contrast component. As a result, not
all the neighbors and some of the non-neighbors will be used to infer the
target. To encourage the two modules above to learn general and robust
representations, we design an adaptive augmentation module that incorporates
data-driven attribute augmentation and centrality-based topology augmentation
over the spatiotemporal Kriging graph data. Extensive experiments on real-world
datasets demonstrate the superior performance of KCP compared to its peers with
6% improvements and exceptional transferability and robustness. The code is
available at https://github.com/bonaldli/KCP