  Graph convolutional networks (GCNs) have gained popularity due to high
performance achievable on several downstream tasks including node
classification. Several architectural variants of these networks have been
proposed and investigated with experimental studies in the literature.
Motivated by a recent work on simplifying GCNs, we study the problem of
designing other variants and propose a framework to compose networks using
building blocks of GCN. The framework offers flexibility to compose and
evaluate different networks using feature and/or label propagation networks,
linear or non-linear networks, with each composition having different
computational complexity. We conduct a detailed experimental study on several
benchmark datasets with many variants and present observations from our
evaluation. Our empirical experimental results suggest that several newly
composed variants are useful alternatives to consider because they are as
competitive as, or better than the original GCN.
