  Networks found in the real-world are numerous and varied. A common type of
network is the heterogeneous network, where the nodes (and edges) can be of
different types. Accordingly, there have been efforts at learning
representations of these heterogeneous networks in low-dimensional space.
However, most of the existing heterogeneous network embedding methods suffer
from the following two drawbacks: (1) The target space is usually Euclidean.
Conversely, many recent works have shown that complex networks may have
hyperbolic latent anatomy, which is non-Euclidean. (2) These methods usually
rely on meta-paths, which require domain-specific prior knowledge for meta-path
selection. Additionally, different down-streaming tasks on the same network
might require different meta-paths in order to generate task-specific
embeddings. In this paper, we propose a novel self-guided random walk method
that does not require meta-path for embedding heterogeneous networks into
hyperbolic space. We conduct thorough experiments for the tasks of network
reconstruction and link prediction on two public datasets, showing that our
model outperforms a variety of well-known baselines across all tasks.
