  Graph neural networks (GNNs) have been widely used in deep learning on
graphs. They can learn effective node representations that achieve superior
performances in graph analysis tasks such as node classification and node
clustering. However, most methods ignore the heterogeneity in real-world
graphs. Methods designed for heterogeneous graphs, on the other hand, fail to
learn complex semantic representations because they only use meta-paths instead
of meta-graphs. Furthermore, they cannot fully capture the content-based
correlations between nodes, as they either do not use the self-attention
mechanism or only use it to consider the immediate neighbors of each node,
ignoring the higher-order neighbors. We propose a novel Higher-order
Attribute-Enhancing (HAE) framework that enhances node embedding in a
layer-by-layer manner. Under the HAE framework, we propose a Higher-order
Attribute-Enhancing Graph Neural Network (HAEGNN) for heterogeneous network
representation learning. HAEGNN simultaneously incorporates meta-paths and
meta-graphs for rich, heterogeneous semantics, and leverages the self-attention
mechanism to explore content-based nodes interactions. The unique higher-order
architecture of HAEGNN allows examining the first-order as well as higher-order
neighborhoods. Moreover, HAEGNN shows good explainability as it learns the
importances of different meta-paths and meta-graphs. HAEGNN is also
memory-efficient, for it avoids per meta-path based matrix calculation.
Experimental results not only show HAEGNN superior performance against the
state-of-the-art methods in node classification, node clustering, and
visualization, but also demonstrate its superiorities in terms of memory
efficiency and explainability.
