  Network Embedding has been widely studied to model and manage data in a
variety of real-world applications. However, most existing works focus on
networks with single-typed nodes or edges, with limited consideration of
unbalanced distributions of nodes and edges. In real-world applications,
networks usually consist of billions of various types of nodes and edges with
abundant attributes. To tackle these challenges, in this paper we propose a
multi-semantic metapath (MSM) model for large scale heterogeneous
representation learning. Specifically, we generate multi-semantic
metapath-based random walks to construct the heterogeneous neighborhood to
handle the unbalanced distributions and propose a unified framework for the
embedding learning. We conduct systematical evaluations for the proposed
framework on two challenging datasets: Amazon and Alibaba. The results
empirically demonstrate that MSM can achieve relatively significant gains over
previous state-of-arts on link prediction.
