  Network embedding is an effective way to solve the network analytics problems
such as node classification, link prediction, etc. It represents network
elements using low dimensional vectors such that the graph structural
information and properties are maximumly preserved. Many prior works focused on
embeddings for networks with the same type of edges or vertices, while some
works tried to generate embeddings for heterogeneous network using mechanisms
like specially designed meta paths. In this paper, we propose two novel
algorithms, GHINE (General Heterogeneous Information Network Embedding) and
AHINE (Adaptive Heterogeneous Information Network Embedding), to compute
distributed representations for elements in heterogeneous networks. Specially,
AHINE uses an adaptive deep model to learn network embeddings that maximizes
the likelihood of preserving the relationship chains between non-adjacent
nodes. We apply our embeddings to a large network of points of interest (POIs)
and achieve superior accuracy on some prediction problems on a ride-hailing
platform. In addition, we show that AHINE outperforms state-of-the-art methods
on a set of learning tasks on public datasets, including node labelling and
similarity ranking in bibliographic networks.
