  Graph Neural Networks (GNNs) have achieved great successes in many learning
tasks performed on graph structures. Nonetheless, to propagate information GNNs
rely on a message passing scheme which can become prohibitively expensive when
working with industrial-scale graphs. Inspired by the PPRGo model, we propose
the CorePPR model, a scalable solution that utilises a learnable convex
combination of the approximate personalised PageRank and the CoreRank to
diffuse multi-hop neighbourhood information in GNNs. Additionally, we
incorporate a dynamic mechanism to select the most influential neighbours for a
particular node which reduces training time while preserving the performance of
the model. Overall, we demonstrate that CorePPR outperforms PPRGo, particularly
on large graphs where selecting the most influential nodes is particularly
relevant for scalability. Our code is publicly available at:
https://github.com/arielramos97/CorePPR.
