Graph Models
AdaGPR
Introduced by Wimalawarne et al. in Layer-wise Adaptive Graph Convolution Networks Using Generalized PagerankAdaGPR is an adaptive, layer-wise graph convolution model. AdaGPR applies adaptive generalized Pageranks at each layer of a GCNII model by learning to predict the coefficients of generalized Pageranks using sparse solvers.
Source: Layer-wise Adaptive Graph Convolution Networks Using Generalized Pagerank
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 |
