  There has been a rising interest in graph neural networks (GNNs) for
representation learning over the past few years. GNNs provide a general and
efficient framework to learn from graph-structured data. However, GNNs
typically only use the information of a very limited neighborhood for each node
to avoid over-smoothing. A larger neighborhood would be desirable to provide
the model with more information. In this work, we incorporate the limit
distribution of Personalized PageRank (PPR) into graph attention networks
(GATs) to reflect the larger neighbor information without introducing
over-smoothing. Intuitively, message aggregation based on Personalized PageRank
corresponds to infinitely many neighborhood aggregation layers. We show that
our models outperform a variety of baseline models for four widely used
benchmark datasets. Our implementation is publicly available online.
