  The graph convolutional networks (GCN) recently proposed by Kipf and Welling
are an effective graph model for semi-supervised learning. This model, however,
was originally designed to be learned with the presence of both training and
test data. Moreover, the recursive neighborhood expansion across layers poses
time and memory challenges for training with large, dense graphs. To relax the
requirement of simultaneous availability of test data, we interpret graph
convolutions as integral transforms of embedding functions under probability
measures. Such an interpretation allows for the use of Monte Carlo approaches
to consistently estimate the integrals, which in turn leads to a batched
training scheme as we propose in this work---FastGCN. Enhanced with importance
sampling, FastGCN not only is efficient for training but also generalizes well
for inference. We show a comprehensive set of experiments to demonstrate its
effectiveness compared with GCN and related models. In particular, training is
orders of magnitude more efficient while predictions remain comparably
accurate.
