{"url":"/dataset/ethereum-nfts-flagged-for-suspected-wash","name":"Ethereum NFTs Flagged for Suspected Wash Trading","full_name":"Ethereum NFTs Flagged for Suspected Wash Trading","description_markdown":"aThis dataset provides NFT ownership traces and detection of potential wash trading activities across several prominent NFT collections on the Ethereum blockchain. The dataset is derived from the methodology presented in the paper \"Beyond the Surface: Advanced Wash Trading Detection in Decentralized NFT Markets\".\r\n\r\nThe approach proposed in the paper is not based on machine learning, but rather on the structural analysis of transaction patterns. It integrates NFT ownership traces with a novel structure called the Linkability Network, which links Ethereum accounts based on their transaction history to identify potentially collusive behavior.\r\n\r\nThe dataset contains:\r\n\r\nNFT Ownership Traces: transfers of NFT ownership from EOAs, including the value in ETH and USD of trades finalized on NFT marketplaces: Blur, OpenSea, Looks Rare, X2Y2 …\r\ndata collected from collection creatio to: 1.5.2022\r\nFlagged NFTs: Each token ID is associated with the number of flagged trades detected by our algorithm.\r\nWe analyzed the following Ethereum NFT collections:\r\n\r\n- Bored Ape Yacht Club\r\n- Mutant Ape Yacht Club\r\n- Azuki\r\n- Clonex\r\n- Meebits\r\n- Bored Ape Kennel Club\r\n- Beanz Official\r\n- Moonbirds\r\n- Hape Prime\r\n- Pudgy Penguins\r\n- VeeFriends\r\nWe are releasing this dataset to fill the gap caused by the absence of a ground truth dataset for validation purposes. Our goal is to facilitate the validation of future studies on NFT wash trading. Additionally, we encourage the machine learning community to leverage this dataset for training models aimed at detecting wash trading in NFTs.\r\n\r\nFor further details on the methodology, and our interpretation of results, please refer to the original publication: Beyond the Surface: Advanced Wash Trading Detection in Decentralized NFT Markets\r\n\r\nWe gratefully acknowledge OpenCloset.ai for providing auxiliary data used to enrich the NFT ownership traces.","description_withheld":null,"homepage":"https://www.kaggle.com/datasets/03370b71c433bfddf931292adbee759704774fa4b414174b14e3ff8946213d8b","introduced_date":"2023-12-27","introduced_date_note":null,"introduced_by":{"paper":"/paper/beyond-the-surface-advanced-wash-trading","title":"Beyond the Surface: Advanced Wash Trading Detection in Decentralized NFT Markets","first_author":null,"url":null},"license":{"name":"Attribution 4.0 International (CC BY 4.0)","url":null},"modalities":[],"tasks":[],"languages":[],"variants":["Ethereum NFTs Flagged for Suspected Wash Trading"],"data_loaders":[],"num_papers_in_archive":1,"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."}