Graph Models

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 Pagerank


Paper Code Results Date Stars


Task Papers Share
Generalization Bounds 1 50.00%
Node Classification 1 50.00%


Component Type
🤖 No Components Found You can add them if they exist; e.g. Mask R-CNN uses RoIAlign