  Graph convolutional networks (GCNs) have been successfully applied in node
classification tasks of network mining. However, most of these models based on
neighborhood aggregation are usually shallow and lack the "graph pooling"
mechanism, which prevents the model from obtaining adequate global information.
In order to increase the receptive field, we propose a novel deep Hierarchical
Graph Convolutional Network (H-GCN) for semi-supervised node classification.
H-GCN first repeatedly aggregates structurally similar nodes to hyper-nodes and
then refines the coarsened graph to the original to restore the representation
for each node. Instead of merely aggregating one- or two-hop neighborhood
information, the proposed coarsening procedure enlarges the receptive field for
each node, hence more global information can be captured. The proposed H-GCN
model shows strong empirical performance on various public benchmark graph
datasets, outperforming state-of-the-art methods and acquiring up to 5.9%
performance improvement in terms of accuracy. In addition, when only a few
labeled samples are provided, our model gains substantial improvements.
