AdaGPR 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 PagerankPaper | Code | Results | Date | Stars |
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🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |