  Graph convolutional networks have been successful in addressing graph-based
tasks such as semi-supervised node classification. Existing methods use a
network structure defined by the user based on experimentation with fixed
number of layers and neurons per layer and employ a layer-wise propagation rule
to obtain the node embeddings. Designing an automatic process to define a
problem-dependant architecture for graph convolutional networks can greatly
help to reduce the need for manual design of the structure of the model in the
training process. In this paper, we propose a method to automatically build
compact and task-specific graph convolutional networks. Experimental results on
widely used publicly available datasets show that the proposed method
outperforms related methods based on convolutional graph networks in terms of
classification performance and network compactness.
