  Neural message passing algorithms for semi-supervised classification on
graphs have recently achieved great success. However, for classifying a node
these methods only consider nodes that are a few propagation steps away and the
size of this utilized neighborhood is hard to extend. In this paper, we use the
relationship between graph convolutional networks (GCN) and PageRank to derive
an improved propagation scheme based on personalized PageRank. We utilize this
propagation procedure to construct a simple model, personalized propagation of
neural predictions (PPNP), and its fast approximation, APPNP. Our model's
training time is on par or faster and its number of parameters on par or lower
than previous models. It leverages a large, adjustable neighborhood for
classification and can be easily combined with any neural network. We show that
this model outperforms several recently proposed methods for semi-supervised
classification in the most thorough study done so far for GCN-like models. Our
implementation is available online.
