  Network embedding is a fervid topic in current networks science and observes
that most real complex systems can be embedded in hidden metrics space and
emerge as the geometrical property, where the geometric distance between nodes
determines the likelihood of links connected. Among those, hyperbolic space
associated with the structural organization of many real complex systems, it
has thus received extensive attention. However, the majority of methods and
measurements, recently developed, less take these features into consideration
for the asymmetry of links. Here, we discuss how to multiplex node information
as an embedding foundation through identifying the bipartite structure of
directed networks; and we proposed the generally mapping framework which
hybrids the topological structure of complex networks, directed links and the
hidden metrics space. By splitting the different properties of a node,
possibilities between different types of nodes can be modeled. In addition to
that, we apply this model to some real systems, including international trade
networks and C.elegans neural networks. Results confirm that directed networks
enable mapping into metrics space as well, and network embedding information
can improve the scope of application of existing models.
