  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 particular, the modularity function
provides a convenient source of information about the community structure of
networks. In this work we investigate the effect on the quality of learned
representations by the incorporation of community structure preservation
objectives of networks in the graph convolutional model. We incorporate the
objectives in two ways, through an explicit regularization term in the cost
function in the output layer and as an additional loss term computed via an
auxiliary layer. We report the effect of community structure preserving terms
in the graph convolutional architectures. Experimental evaluation on two
attributed bibilographic networks showed that the incorporation of the
community-preserving objective improves semi-supervised node classification
accuracy in the sparse label regime.
