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We present, GeniePath, a scalable approach for learning adaptive receptivefields of neural networks defined on permutation invariant graph data. InGeniePath, we propose an adaptive path layer consists of two complementaryfunctions designed for breadth and depth exploration respectively, where theformer learns the importance of different sized neighborhoods, while the latterextracts and filters signals aggregated from neighbors of different hops away.Our method works in both transductive and inductive settings, and extensiveexperiments compared with competitive methods show that our approaches yieldstate-of-the-art results on large graphs.

GeniePath: Graph Neural Networks with Adaptive Receptive Paths