  Heterogeneous graphs are ubiquitous in real-world applications because they
can represent various relationships between different types of entities.
Therefore, learning embeddings in such graphs is a critical problem in graph
machine learning. However, existing solutions for this problem fail to scale to
large heterogeneous graphs due to their high computational complexity. To
address this issue, we propose a Multi-Level Embedding framework of nodes on a
heterogeneous graph (HeteroMILE) - a generic methodology that allows
contemporary graph embedding methods to scale to large graphs. HeteroMILE
repeatedly coarsens the large sized graph into a smaller size while preserving
the backbone structure of the graph before embedding it, effectively reducing
the computational cost by avoiding time-consuming processing operations. It
then refines the coarsened embedding to the original graph using a
heterogeneous graph convolution neural network. We evaluate our approach using
several popular heterogeneous graph datasets. The experimental results show
that HeteroMILE can substantially reduce computational time (approximately 20x
speedup) and generate an embedding of better quality for link prediction and
node classification.
