Graph Models
GeniePath
Introduced by Liu et al. in GeniePath: Graph Neural Networks with Adaptive Receptive PathsGeniePath 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
Source: GeniePath: Graph Neural Networks with Adaptive Receptive Paths
Papers
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Usage Over Time
Components
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| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
