{"url":"/method/adagpr","slug":"adagpr","name":"AdaGPR","full_name":"AdaGPR","full_name_withheld":false,"description_markdown":"**AdaGPR** is an adaptive, layer-wise graph [convolution](https://paperswithcode.com/method/convolution) model. AdaGPR applies adaptive generalized Pageranks at each layer of a [GCNII](https://paperswithcode.com/method/gcnii) model by learning to predict the coefficients of generalized Pageranks using sparse solvers.","description_state":"present","introduced_year":null,"introduced_by":{"title":"Layer-wise Adaptive Graph Convolution Networks Using Generalized Pagerank","paper":"/paper/adaptive-and-interpretable-graph-convolution","first_author":"Kishan Wimalawarne","n_authors":2,"url_abs":null,"archive_paper_url":"https://paperswithcode.com/paper/adaptive-and-interpretable-graph-convolution"},"source":{"url":"https://arxiv.org/abs/2108.10636v3","title":"Layer-wise Adaptive Graph Convolution Networks Using Generalized Pagerank","url_on_a_paper_host":true},"code_snippet_url":null,"code_snippet_url_on_a_code_host":false,"categories":[{"area":"Graphs","area_id":"graphs","collection":"Graph Models","url":"/methods/category/graph-models","pwc_aliases":[]}],"n_papers_tagged":1,"archive_num_papers":1,"papers_newest_first":[{"paper":"/paper/adaptive-and-interpretable-graph-convolution","title":"Layer-wise Adaptive Graph Convolution Networks Using Generalized Pagerank","date":"2021-08-24","arxiv_id":"2108.10636","n_code_links":0,"syntology":null}],"papers_shown":1,"tasks":[{"task":"/task/generalization-bounds","name":"Generalization Bounds","papers":1},{"task":"/task/node-classification","name":"Node Classification","papers":1}],"tasks_shown":2,"n_tasks":2,"usage_by_year":[{"year":"2021","papers":1}],"row_source":"methods_table","archive":{"source":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","archive_url":"https://paperswithcode.com/method/adagpr"},"syntology_read_at":"2026-09-24T18:15:14+00:00"}