  In recent years, Graph Convolutional Networks (GCNs) and their variants have
been widely utilized in learning tasks that involve graphs. These tasks include
recommendation systems, node classification, among many others. In node
classification problem, the input is a graph in which the edges represent the
association between pairs of nodes, multi-dimensional feature vectors are
associated with the nodes, and some of the nodes in the graph have known
labels. The objective is to predict the labels of the nodes that are not
labeled, using the nodes features, in conjunction with graph topology. While
GCNs have been successfully applied to this problem, the caveats that they
inherit from traditional deep learning models pose significant challenges to
broad utilization of GCNs in node classification. One such caveat is that
training a GCN requires a large number of labeled training instances, which is
often not the case in realistic settings. To remedy this requirement,
state-of-the-art methods leverage network diffusion-based approaches to
propagate labels across the network before training GCNs. However, these
approaches ignore the tendency of the network diffusion methods in biasing
proximity with centrality, resulting in the propagation of labels to the nodes
that are well-connected in the graph. To address this problem, here we present
an alternate approach to extrapolating node labels in GCNs in the following
three steps: (i) clustering of the network to identify communities, (ii) use of
network diffusion algorithms to quantify the proximity of each node to the
communities, thereby obtaining a low-dimensional topological profile for each
node, (iii) comparing these topological profiles to identify nodes that are
most similar to the labeled nodes.
