  Bi-type multi-relational heterogeneous graph (BMHG) is one of the most common
graphs in practice, for example, academic networks, e-commerce user behavior
graph and enterprise knowledge graph. It is a critical and challenge problem on
how to learn the numerical representation for each node to characterize subtle
structures. However, most previous studies treat all node relations in BMHG as
the same class of relation without distinguishing the different characteristics
between the intra-class relations and inter-class relations of the bi-typed
nodes, causing the loss of significant structure information. To address this
issue, we propose a novel Dual Hierarchical Attention Networks (DHAN) based on
the bi-typed multi-relational heterogeneous graphs to learn comprehensive node
representations with the intra-class and inter-class attention-based encoder
under a hierarchical mechanism. Specifically, the former encoder aggregates
information from the same type of nodes, while the latter aggregates node
representations from its different types of neighbors. Moreover, to
sufficiently model node multi-relational information in BMHG, we adopt a newly
proposed hierarchical mechanism. By doing so, the proposed dual hierarchical
attention operations enable our model to fully capture the complex structures
of the bi-typed multi-relational heterogeneous graphs. Experimental results on
various tasks against the state-of-the-arts sufficiently confirm the capability
of DHAN in learning node representations on the BMHGs.
