  A heterogeneous graph consists of different vertices and edges types.
Learning on heterogeneous graphs typically employs meta-paths to deal with the
heterogeneity by reducing the graph to a homogeneous network, guide random
walks or capture semantics. These methods are however sensitive to the choice
of meta-paths, with suboptimal paths leading to poor performance. In this
paper, we propose an approach for learning on heterogeneous graphs without
using meta-paths. Specifically, we decompose a heterogeneous graph into
different homogeneous relation-type graphs, which are then combined to create
higher-order relation-type representations. These representations preserve the
heterogeneity of edges and retain their edge directions while capturing the
interaction of different vertex types multiple hops apart. This is then
complemented with attention mechanisms to distinguish the importance of the
relation-type based neighbors and the relation-types themselves. Experiments
demonstrate that our model generally outperforms other state-of-the-art
baselines in the vertex classification task on three commonly studied
heterogeneous graph datasets.
