  Spatiotemporal learning, which aims at extracting spatiotemporal correlations
from the collected spatiotemporal data, is a research hotspot in recent years.
And considering the inherent graph structure of spatiotemporal data, recent
works focus on capturing spatial dependencies by utilizing Graph Convolutional
Networks (GCNs) to aggregate vertex features with the guidance of adjacency
matrices. In this paper, with extensive and deep-going experiments, we
comprehensively analyze existing spatiotemporal graph learning models and
reveal that extracting adjacency matrices with carefully design strategies,
which are viewed as the key of enhancing performance on graph learning, are
largely ineffective. Meanwhile, based on these experiments, we also discover
that the aggregation itself is more important than the way that how vertices
are aggregated. With these preliminary, a novel efficient Graph-Free Spatial
(GFS) learning module based on layer normalization for capturing spatial
correlations in spatiotemporal graph learning. The proposed GFS module can be
easily plugged into existing models for replacing all graph convolution
components. Rigorous theoretical proof demonstrates that the time complexity of
GFS is significantly better than that of graph convolution operation. Extensive
experiments verify the superiority of GFS in both the perspectives of
efficiency and learning effect in processing graph-structured data especially
extreme large scale graph data.
