  Graph convolutional neural networks (GCNN) have numerous applications in
different graph based learning tasks. Although the techniques obtain impressive
results, they often fall short in accounting for the uncertainty associated
with the underlying graph structure. In the recently proposed Bayesian GCNN
(BGCN) framework, this issue is tackled by viewing the observed graph as a
sample from a parametric random graph model and targeting joint inference of
the graph and the GCNN weights. In this paper, we introduce an alternative
generative model for graphs based on copying nodes and incorporate it within
the BGCN framework. Our approach has the benefit that it uses information
provided by the node features and training labels in the graph topology
inference. Experiments show that the proposed algorithm compares favorably to
the state-of-the-art in benchmark node classification tasks.
