  Graph sparsification is a powerful tool to approximate an arbitrary graph and
has been used in machine learning over homogeneous graphs. In heterogeneous
graphs such as knowledge graphs, however, sparsification has not been
systematically exploited to improve efficiency of learning tasks. In this work,
we initiate the study on heterogeneous graph sparsification and develop
sampling-based algorithms for constructing sparsifiers that are provably sparse
and preserve important information in the original graphs. We have performed
extensive experiments to confirm that the proposed method can improve time and
space complexities of representation learning while achieving comparable, or
even better performance in subsequent graph learning tasks based on the learned
embedding.
