  Heterogeneous information networks (HINs) have been extensively applied to
real-world tasks, such as recommendation systems, social networks, and citation
networks. While existing HIN representation learning methods can effectively
learn the semantic and structural features in the network, little awareness was
given to the distribution discrepancy of subgraphs within a single HIN.
However, we find that ignoring such distribution discrepancy among subgraphs
from multiple sources would hinder the effectiveness of graph embedding
learning algorithms. This motivates us to propose SUMSHINE (Scalable
Unsupervised Multi-Source Heterogeneous Information Network Embedding) -- a
scalable unsupervised framework to align the embedding distributions among
multiple sources of an HIN. Experimental results on real-world datasets in a
variety of downstream tasks validate the performance of our method over the
state-of-the-art heterogeneous information network embedding algorithms.
