  One of the challenges in studying the interactions in large graphs is to
learn their diverse pattern and various interaction types. Hence, considering
only one distribution and model to study all nodes and ignoring their diversity
and local features in their neighborhoods, might severely affect the overall
performance. Based on the structural information of the nodes in the graph and
the interactions between them, the main graph can be divided into multiple
sub-graphs. This graph partitioning can tremendously affect the learning
process, however the overall performance is highly dependent on the clustering
method to avoid misleading the model. In this work, we present a new framework
called KD-SGL to effectively learn the sub-graphs, where we define one global
model to learn the overall structure of the graph and multiple local models for
each sub-graph. We assess the performance of the proposed framework and
evaluate it on public datasets. Based on the achieved results, it can improve
the performance of the state-of-the-arts spatiotemporal models with comparable
results compared to ensemble of models with less complexity.
