Since several types of data can be represented as graphs, there has been a demand for
generalizing neural network models for graph data. Graph convolution is a recent scalable method
for performing deep feature learning on attributed graphs by aggregating local node information
over multiple layers. Such layers only consider attribute information of node neighbors in the
forward model and do not incorporate knowledge of global network structure in the learning task. In
this paper, we present a scalable semi-supervised learning method for graph-structured data which
considers not only neighbors information, but also the global network structure. In our method, we
add a term preserving the network structural features such as centrality to the objective function
of Graph Convolutional Network and train for both node classification and network structure
preservation simultaneously. Experimental results showed that our method outperforms state-of-the-art
baselines for the node classification tasks in the sparse label regime.
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