  Heterogeneous graph neural networks aim to discover discriminative node
embeddings and relations from multi-relational networks.One challenge of
heterogeneous graph learning is the design of learnable meta-paths, which
significantly influences the quality of learned embeddings.Thus, in this paper,
we propose an Attributed Multi-Order Graph Convolutional Network (AMOGCN),
which automatically studies meta-paths containing multi-hop neighbors from an
adaptive aggregation of multi-order adjacency matrices. The proposed model
first builds different orders of adjacency matrices from manually designed node
connections. After that, an intact multi-order adjacency matrix is attached
from the automatic fusion of various orders of adjacency matrices. This process
is supervised by the node semantic information, which is extracted from the
node homophily evaluated by attributes. Eventually, we utilize a one-layer
simplifying graph convolutional network with the learned multi-order adjacency
matrix, which is equivalent to the cross-hop node information propagation with
multi-layer graph neural networks. Substantial experiments reveal that AMOGCN
gains superior semi-supervised classification performance compared with
state-of-the-art competitors.
