GeniePath
GeniePath
GeniePath is 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.

Description and image from: [GeniePath: Graph Neural Networks with Adaptive Receptive Paths](https://arxiv.org/pdf/1802.00910.pdf)
GeniePath: Graph Neural Networks with Adaptive Receptive Paths
https://paperswithcode.com/paper/geniepath-graph-neural-networks-with-adaptive
GeniePath: Graph Neural Networks with Adaptive Receptive Paths