  Real-world networks and knowledge graphs are usually heterogeneous networks.
Representation learning on heterogeneous networks is not only a popular but a
pragmatic research field. The main challenge comes from the heterogeneity --
the diverse types of nodes and edges. Besides, for a given node in a HIN, the
significance of a neighborhood node depends not only on the structural distance
but semantics. How to effectively capture both structural and semantic
relations is another challenge. The current state-of-the-art methods are based
on the algorithm of meta-path and therefore have a serious disadvantage -- the
performance depends on the arbitrary choosing of meta-path(s). However, the
selection of meta-path(s) is experience-based and time-consuming. In this work,
we propose a novel meta-path-free representation learning on heterogeneous
networks, namely Heterogeneous graph Convolutional Networks (HCN). The proposed
method fuses the heterogeneity and develops a $k$-strata algorithm ($k$ is an
integer) to capture the $k$-hop structural and semantic information in
heterogeneous networks. To the best of our knowledge, this is the first attempt
to break out of the confinement of meta-paths for representation learning on
heterogeneous networks. We carry out extensive experiments on three real-world
heterogeneous networks. The experimental results demonstrate that the proposed
method significantly outperforms the current state-of-the-art methods in a
variety of analytic tasks.
