  We present, GeniePath, a scalable approach for learning adaptive receptive
fields of neural networks defined on permutation invariant graph data. In
GeniePath, we propose an adaptive path layer consists of two complementary
functions designed for breadth and depth exploration respectively, where the
former learns the importance of different sized neighborhoods, while the latter
extracts and filters signals aggregated from neighbors of different hops away.
Our method works in both transductive and inductive settings, and extensive
experiments compared with competitive methods show that our approaches yield
state-of-the-art results on large graphs.
