  We extend the graph convolutional network method for deep learning on graph
data to higher order in terms of neighboring nodes. In order to construct
representations for a node in a graph, in addition to the features of the node
and its immediate neighboring nodes, we also include more distant nodes in the
calculations. In experimenting with a number of publicly available citation
graph datasets, we show that this higher order neighbor visiting pays off by
outperforming the original model especially when we have a limited number of
available labeled data points for the training of the model.
