  The real-world networks often compose of different types of nodes and edges
with rich semantics, widely known as heterogeneous information network (HIN).
Heterogeneous network embedding aims to embed nodes into low-dimensional
vectors which capture rich intrinsic information of heterogeneous networks.
However, existing models either depend on manually designing meta-paths, ignore
mutual effects between different semantics, or omit some aspects of information
from global networks. To address these limitations, we propose a novel
Graph-Aggregated Heterogeneous Network Embedding (GAHNE), which is designed to
extract the semantics of HINs as comprehensively as possible to improve the
results of downstream tasks based on graph convolutional neural networks. In
GAHNE model, we develop several mechanisms that can aggregate semantic
representations from different single-type sub-networks as well as fuse the
global information into final embeddings. Extensive experiments on three
real-world HIN datasets show that our proposed model consistently outperforms
the existing state-of-the-art methods.
