  Graph Convolutional Network (GCN) has achieved extraordinary success in
learning effective task-specific representations of nodes in graphs. However,
regarding Heterogeneous Information Network (HIN), existing HIN-oriented GCN
methods still suffer from two deficiencies: (1) they cannot flexibly explore
all possible meta-paths and extract the most useful ones for a target object,
which hinders both effectiveness and interpretability; (2) they often need to
generate intermediate meta-path based dense graphs, which leads to high
computational complexity. To address the above issues, we propose an
interpretable and efficient Heterogeneous Graph Convolutional Network (ie-HGCN)
to learn the representations of objects in HINs. It is designed as a
hierarchical aggregation architecture, i.e., object-level aggregation first,
followed by type-level aggregation. The novel architecture can automatically
extract useful meta-paths for each object from all possible meta-paths (within
a length limit), which brings good model interpretability. It can also reduce
the computational cost by avoiding intermediate HIN transformation and
neighborhood attention. We provide theoretical analysis about the proposed
ie-HGCN in terms of evaluating the usefulness of all possible meta-paths, its
connection to the spectral graph convolution on HINs, and its quasi-linear time
complexity. Extensive experiments on three real network datasets demonstrate
the superiority of ie-HGCN over the state-of-the-art methods.
