  Graph convolutional network (GCN) based approaches have achieved significant
progress for solving complex, graph-structured problems. GCNs incorporate the
graph structure information and the node (or edge) features through message
passing and computes 'deep' node representations. Despite significant progress
in the field, designing GCN architectures for heterogeneous graphs still
remains an open challenge. Due to the schema of a heterogeneous graph, useful
information may reside multiple hops away. A key question is how to perform
message passing to incorporate information of neighbors multiple hops away
while avoiding the well-known over-smoothing problem in GCNs. To address this
question, we propose our GCN framework 'Deep Heterogeneous Graph Convolutional
Network (DHGCN)', which takes advantage of the schema of a heterogeneous graph
and uses a hierarchical approach to effectively utilize information many hops
away. It first computes representations of the target nodes based on their
'schema-derived ego-network' (SEN). It then links the nodes of the same type
with various pre-defined metapaths and performs message passing along these
links to compute final node representations. Our design choices naturally
capture the way a heterogeneous graph is generated from the schema. The
experimental results on real and synthetic datasets corroborate the design
choice and illustrate the performance gains relative to competing alternatives.
