{"url":"/dataset/grb","name":"GRB","full_name":"Graph Robustness Benchmark","description_markdown":"**Graph Robustness Benchmark** (**GRB**) provides scalable, unified, modular, and reproducible evaluation on the adversarial robustness of graph machine learning models. GRB has elaborated datasets, unified evaluation pipeline, modular coding framework, and reproducible leaderboards, which facilitate the developments of graph adversarial learning, summarizing existing progress and generating insights into future research.\r\n\r\nGitHub: [https://github.com/thudm/grb](https://github.com/thudm/grb)","description_withheld":null,"homepage":"https://cogdl.ai/grb/home","introduced_date":"2021-11-08","introduced_date_note":null,"introduced_by":{"paper":"/paper/graph-robustness-benchmark-benchmarking-the","title":"Graph Robustness Benchmark: Benchmarking the Adversarial Robustness of Graph Machine Learning","first_author":"Qinkai Zheng","url":null},"license":{"name":"Unknown","url":null},"modalities":[{"name":"Graphs","url":"/datasets/modality/graphs"}],"tasks":[],"languages":[],"variants":["GRB"],"data_loaders":[{"repo":"https://github.com/thudm/grb","url":"https://github.com/thudm/grb","frameworks":[]}],"num_papers_in_archive":6,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}