Mapping the NFT revolution: market trends, trade networks, and visual features
Non Fungible Tokens (NFTs) are digital assets that represent objects like art, collectible, and in-game items. They are traded online, often with cryptocurrency, and are generally encoded within smart contracts on a blockchain. Public attention towards NFTs has exploded in 2021, when their market ha...
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| Published in | Scientific reports Vol. 11; no. 1; pp. 20902 - 11 |
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| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
London
Nature Publishing Group UK
22.10.2021
Nature Publishing Group Nature Portfolio |
| Subjects | |
| Online Access | Get full text |
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| Abstract | Non Fungible Tokens (NFTs) are digital assets that represent objects like art, collectible, and in-game items. They are traded online, often with cryptocurrency, and are generally encoded within smart contracts on a blockchain. Public attention towards NFTs has exploded in 2021, when their market has experienced record sales, but little is known about the overall structure and evolution of its market. Here, we analyse data concerning 6.1 million trades of 4.7 million NFTs between June 23, 2017 and April 27, 2021, obtained primarily from Ethereum and WAX blockchains. First, we characterize statistical properties of the market. Second, we build the network of interactions, show that traders typically specialize on NFTs associated with similar objects and form tight clusters with other traders that exchange the same kind of objects. Third, we cluster objects associated to NFTs according to their visual features and show that collections contain visually homogeneous objects. Finally, we investigate the predictability of NFT sales using simple machine learning algorithms and find that sale history and, secondarily, visual features are good predictors for price. We anticipate that these findings will stimulate further research on NFT production, adoption, and trading in different contexts. |
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| AbstractList | Non Fungible Tokens (NFTs) are digital assets that represent objects like art, collectible, and in-game items. They are traded online, often with cryptocurrency, and are generally encoded within smart contracts on a blockchain. Public attention towards NFTs has exploded in 2021, when their market has experienced record sales, but little is known about the overall structure and evolution of its market. Here, we analyse data concerning 6.1 million trades of 4.7 million NFTs between June 23, 2017 and April 27, 2021, obtained primarily from Ethereum and WAX blockchains. First, we characterize statistical properties of the market. Second, we build the network of interactions, show that traders typically specialize on NFTs associated with similar objects and form tight clusters with other traders that exchange the same kind of objects. Third, we cluster objects associated to NFTs according to their visual features and show that collections contain visually homogeneous objects. Finally, we investigate the predictability of NFT sales using simple machine learning algorithms and find that sale history and, secondarily, visual features are good predictors for price. We anticipate that these findings will stimulate further research on NFT production, adoption, and trading in different contexts. Non Fungible Tokens (NFTs) are digital assets that represent objects like art, collectible, and in-game items. They are traded online, often with cryptocurrency, and are generally encoded within smart contracts on a blockchain. Public attention towards NFTs has exploded in 2021, when their market has experienced record sales, but little is known about the overall structure and evolution of its market. Here, we analyse data concerning 6.1 million trades of 4.7 million NFTs between June 23, 2017 and April 27, 2021, obtained primarily from Ethereum and WAX blockchains. First, we characterize statistical properties of the market. Second, we build the network of interactions, show that traders typically specialize on NFTs associated with similar objects and form tight clusters with other traders that exchange the same kind of objects. Third, we cluster objects associated to NFTs according to their visual features and show that collections contain visually homogeneous objects. Finally, we investigate the predictability of NFT sales using simple machine learning algorithms and find that sale history and, secondarily, visual features are good predictors for price. We anticipate that these findings will stimulate further research on NFT production, adoption, and trading in different contexts.Non Fungible Tokens (NFTs) are digital assets that represent objects like art, collectible, and in-game items. They are traded online, often with cryptocurrency, and are generally encoded within smart contracts on a blockchain. Public attention towards NFTs has exploded in 2021, when their market has experienced record sales, but little is known about the overall structure and evolution of its market. Here, we analyse data concerning 6.1 million trades of 4.7 million NFTs between June 23, 2017 and April 27, 2021, obtained primarily from Ethereum and WAX blockchains. First, we characterize statistical properties of the market. Second, we build the network of interactions, show that traders typically specialize on NFTs associated with similar objects and form tight clusters with other traders that exchange the same kind of objects. Third, we cluster objects associated to NFTs according to their visual features and show that collections contain visually homogeneous objects. Finally, we investigate the predictability of NFT sales using simple machine learning algorithms and find that sale history and, secondarily, visual features are good predictors for price. We anticipate that these findings will stimulate further research on NFT production, adoption, and trading in different contexts. Abstract Non Fungible Tokens (NFTs) are digital assets that represent objects like art, collectible, and in-game items. They are traded online, often with cryptocurrency, and are generally encoded within smart contracts on a blockchain. Public attention towards NFTs has exploded in 2021, when their market has experienced record sales, but little is known about the overall structure and evolution of its market. Here, we analyse data concerning 6.1 million trades of 4.7 million NFTs between June 23, 2017 and April 27, 2021, obtained primarily from Ethereum and WAX blockchains. First, we characterize statistical properties of the market. Second, we build the network of interactions, show that traders typically specialize on NFTs associated with similar objects and form tight clusters with other traders that exchange the same kind of objects. Third, we cluster objects associated to NFTs according to their visual features and show that collections contain visually homogeneous objects. Finally, we investigate the predictability of NFT sales using simple machine learning algorithms and find that sale history and, secondarily, visual features are good predictors for price. We anticipate that these findings will stimulate further research on NFT production, adoption, and trading in different contexts. |
| ArticleNumber | 20902 |
| Author | Di Giacinto, Flavio Nadini, Matthieu Alessandretti, Laura Aiello, Luca Maria Baronchelli, Andrea Martino, Mauro |
| Author_xml | – sequence: 1 givenname: Matthieu surname: Nadini fullname: Nadini, Matthieu organization: Department of Mathematics, City University of London, The Alan Turing Institute, British Library – sequence: 2 givenname: Laura orcidid: 0000-0001-6003-1165 surname: Alessandretti fullname: Alessandretti, Laura organization: Technical University of Denmark – sequence: 3 givenname: Flavio surname: Di Giacinto fullname: Di Giacinto, Flavio organization: Department of Mathematics, City University of London, Department of Neuroscience, Catholic University of the Sacred Heart – sequence: 4 givenname: Mauro surname: Martino fullname: Martino, Mauro organization: IBM Research – sequence: 5 givenname: Luca Maria surname: Aiello fullname: Aiello, Luca Maria organization: IT University of Copenhagen – sequence: 6 givenname: Andrea surname: Baronchelli fullname: Baronchelli, Andrea email: abaronchelli@turing.ac.uk organization: Department of Mathematics, City University of London, The Alan Turing Institute, British Library, UCL Centre for Blockchain Technologies, University College London |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/34686678$$D View this record in MEDLINE/PubMed |
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| DOI | 10.1038/s41598-021-00053-8 |
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Results: Beeple’s purely digital NFT-based work of Art achieves \$69.3 million at Christie’s. (2021). https://www.christies.com/about-us/press-archive/details?PressReleaseID=9970&lid=1. (Accessed 4 May 2021). (Christie’s Press Release). Team, C. (2021). CryptoKitties: Collect and breed furrever friends. https://www.cryptokitties.co/. (Accessed 4 May 2021). (Cryptokitties). FranceschetMArt for spaceJ. Comput. Cultural Heritage (JOCCH)2020131910.1145/3402443 PreisTMoatHSStanleyHEQuantifying trading behavior in financial markets using google trendsSci. Rep.201331610.1038/srep01684 KrizhevskyASutskeverIHintonGEImagenet classification with deep convolutional neural networksAdv. Neural Inf. Process. Syst.20122510971105 Devlin, J. The “insane” money in trading collectible cards. (2021). https://www.bbc.co.uk/news/business-56413186. (Accessed 20 May 2021). (BBC). Dowling, M. Fertile land: Pricing non-fungible tokens. Finance Res. Lett. 102096, https://doi.org/10.1016/j.frl.2021.102096 (2021). 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(Accessed 20 May 2021). (Decrypt). Ben Luke, A. S. & Stoilas, H. WTF are NFTs? Why crypto is dominating the Art market. (2021). https://www.theartnewspaper.com/podcast/wtf-nfts. (Accessed 4 May 2021). (The Art Newspaper). ElBahrawyAAlessandrettiLKandlerAPastor-SatorrasRBaronchelliAEvolutionary dynamics of the cryptocurrency marketR. Soc. Open Sci.20174170623374598610.1098/rsos.170623 Jolliffe, I. T. Principal components in regression analysis. in Principal Component Analysis, 129–155 (Springer, 1986). Sako, K., Matsuo, S. & Meier, S. Fairness in ERC token markets: A case study of CryptoKitties. arXiv preprintarXiv:2102.03721 (2021). Westerkamp, M., Victor, F. & Küpper, A. Blockchain-based supply chain traceability: Token recipes model manufacturing processes. in 2018 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), 1595–1602 (IEEE, 2018). WhitakerAArt and blockchain: A primer, history, and taxonomy of blockchain use cases in the ArtsArtivate20198214610.1353/artv.2019.0008 BarratAWeigtMOn the properties of small-world network modelsEur. Phys. J. B-Condensed Matter Complex Syst.2000135475601:CAS:528:DC%2BD3cXhsVGrs7w%3D10.1007/s100510050067 Wang, Q., Li, R., Wang, Q. & Chen, S. Non-fungible token (NFT): Overview, evaluation, opportunities and challenges. arXiv preprintarXiv:2105.07447 (2021). KhanASohailAZahooraUQureshiASA survey of the recent architectures of deep convolutional neural networksArtif. Intell. Rev.2020535455551610.1007/s10462-020-09825-6 Alessandretti, L., ElBahrawy, A., Aiello, L. M. & Baronchelli, A. Anticipating cryptocurrency prices using machine learning. Complexity. 8983590, https://doi.org/10.1155/2018/8983590 (2018). de la Rouviere, N. A subgraph to index & explore CryptoKitties auctions. (2021). https://thegraph.com/explorer/subgraph/nieldlr/cryptokitties-sales. (Accessed 4 May 2021). (The Graph). Team, A. M. Atomic market API. (2021). https://wax.api.atomicassets.io/atomicmarket/docs/swagger/. (Accessed 4 May 2021). OpenSea, T. Discover, collect, and sell extraordinary NFTs. (2021). https://opensea.io/. (Accessed 28 May 2021). (OpenSea). ClausetAShaliziCRNewmanMEPower-law distributions in empirical dataSIAM Rev.2009516617032009SIAMR..51..661C256382910.1137/070710111 Team, O. API overview. (2021). https://docs.opensea.io/reference. (Accessed 4 May 2021). (OpenSea). BarratABarthelemyMPastor-SatorrasRVespignaniAThe architecture of complex weighted networksProc. Natl. Acad. 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(The Graph). 53_CR10 HS Moat (53_CR38) 2013; 3 A Clauset (53_CR26) 2009; 51 A Whitaker (53_CR15) 2019; 8 ME Newman (53_CR28) 2003; 67 A ElBahrawy (53_CR36) 2017; 4 53_CR19 E Nuutila (53_CR30) 1994; 49 Y Freund (53_CR32) 1999; 14 53_CR14 M Franceschet (53_CR21) 2020; 13 53_CR13 A Serada (53_CR17) 2021; 16 53_CR35 53_CR12 53_CR11 53_CR18 A ElBahrawy (53_CR39) 2019; 2 53_CR16 X Xu (53_CR34) 2019; 328 53_CR9 53_CR8 T Preis (53_CR37) 2013; 3 53_CR43 53_CR20 53_CR42 53_CR41 53_CR40 U Aslak (53_CR31) 2019; 4 53_CR2 53_CR1 53_CR7 53_CR6 53_CR5 53_CR4 A Barrat (53_CR23) 2000; 13 A Clauset (53_CR29) 2004; 70 A Krizhevsky (53_CR46) 2012; 25 53_CR25 53_CR47 53_CR24 53_CR45 TM Evans (53_CR3) 2019; 47 53_CR22 A Khan (53_CR33) 2020; 53 53_CR44 A Barrat (53_CR27) 2004; 101 53_CR48 |
| References_xml | – reference: Team, N. Non-fungible tokens quarterly report Q1 2021. (2021). https://nonfungible.com/subscribe/nft-report-q1-2021. (Accessed 4 May 2021). (NonFungible Corporation). – reference: Reyburn, S. JPG file sells for \$69 million, as “NFT mania” gathers pace. (2021). https://www.nytimes.com/2021/03/11/arts/design/nft-auction-christies-beeple.html. (Accessed 4 May 2021). (The New York Times). – reference: Dowling, M. Is non-fungible token pricing driven by cryptocurrencies? Finance Res. Lett. 102097, ISSN 1544-6123. https://doi.org/10.1016/j.frl.2021.102097 (2021). – reference: KhanASohailAZahooraUQureshiASA survey of the recent architectures of deep convolutional neural networksArtif. Intell. Rev.2020535455551610.1007/s10462-020-09825-6 – reference: NuutilaESoisalon-SoininenEOn finding the strongly connected components in a directed graphInf. Process. Lett.199449914126491810.1016/0020-0190(94)90047-7 – reference: Barabási, A.-L. The Art market often works in secret. Here’s a look inside. (2021). https://www.nytimes.com/2021/05/07/opinion/nft-art-market.html. (Accessed 17 May 2021). (The New York Times). – reference: AslakUMaierBFNetwulf: Interactive visualization of networks in pythonJ. Open Source Softw.2019414252019JOSS....4.1425A10.21105/joss.01425 – reference: Paszke, A. et al. Pytorch: An imperative style, high-performance deep learning library. arXiv preprintarXiv:1912.01703 (2019). – reference: Dowling, M. Fertile land: Pricing non-fungible tokens. Finance Res. Lett. 102096, https://doi.org/10.1016/j.frl.2021.102096 (2021). – reference: MoatHSQuantifying wikipedia usage patterns before stock market movesSci. Rep.201331510.1038/srep01801 – reference: Team, A. M. Atomic market API. (2021). https://wax.api.atomicassets.io/atomicmarket/docs/swagger/. (Accessed 4 May 2021). – reference: FranceschetMArt for spaceJ. Comput. Cultural Heritage (JOCCH)2020131910.1145/3402443 – reference: Team, N. The best place to analyze, track, and discover NFTs. (2021). https://nonfungible.com/. (Accessed 4 May 2021). (NonFungible Corporation). – reference: NewmanMEMixing patterns in networksPhys. Rev. E2003670261262003PhRvE..67b6126N19751931:STN:280:DC%2BD3s7itlOrtA%3D%3D10.1103/PhysRevE.67.026126 – reference: KrizhevskyASutskeverIHintonGEImagenet classification with deep convolutional neural networksAdv. Neural Inf. Process. Syst.20122510971105 – reference: Team, C. (2021). CryptoKitties: Collect and breed furrever friends. https://www.cryptokitties.co/. (Accessed 4 May 2021). (Cryptokitties). – reference: OpenSea, T. Discover, collect, and sell extraordinary NFTs. (2021). https://opensea.io/. (Accessed 28 May 2021). (OpenSea). – reference: Alessandretti, L., ElBahrawy, A., Aiello, L. M. & Baronchelli, A. Anticipating cryptocurrency prices using machine learning. Complexity. 8983590, https://doi.org/10.1155/2018/8983590 (2018). – reference: Sako, K., Matsuo, S. & Meier, S. Fairness in ERC token markets: A case study of CryptoKitties. arXiv preprintarXiv:2102.03721 (2021). – reference: van Haaften-Schick, L. & Whitaker, A. From the artist’s contract to the blockchain ledger: New forms of artists’ funding using NFTs, fractional equity, and resale royalties. Available at SSRN 3842210, https://doi.org/10.2139/ssrn.3842210 (2021). – reference: PreisTMoatHSStanleyHEQuantifying trading behavior in financial markets using google trendsSci. Rep.201331610.1038/srep01684 – reference: Team, D. Decentraland marketplace. (2021). https://thegraph.com/explorer/subgraph/decentraland/marketplace. (Accessed 4 May 2021). (The Graph). – reference: Jolliffe, I. T. Principal components in regression analysis. in Principal Component Analysis, 129–155 (Springer, 1986). – reference: XuXLiangTZhuJZhengDSunTReview of classical dimensionality reduction and sample selection methods for large-scale data processingNeurocomputing201932851510.1016/j.neucom.2018.02.100 – reference: Riegelhaupt, R. Results: Beeple’s purely digital NFT-based work of Art achieves \$69.3 million at Christie’s. (2021). https://www.christies.com/about-us/press-archive/details?PressReleaseID=9970&lid=1. (Accessed 4 May 2021). (Christie’s Press Release). – reference: ElBahrawyAAlessandrettiLBaronchelliAWikipedia and cryptocurrencies: Interplay between collective attention and market performanceFront. Blockchain201921210.3389/fbloc.2019.00012 – reference: Howcroft, E. “Cryptopunk” NFT sells for \$11.8 million at Sotheby’s. (2021). https://www.reuters.com/technology/cryptopunk-nft-sells-118-million-sothebys-2021-06-10/. (Accessed 25 June 2021). (Reuters). – reference: Devlin, J. The “insane” money in trading collectible cards. (2021). https://www.bbc.co.uk/news/business-56413186. (Accessed 20 May 2021). (BBC). – reference: de la Rouviere, N. A subgraph to index & explore CryptoKitties auctions. (2021). https://thegraph.com/explorer/subgraph/nieldlr/cryptokitties-sales. (Accessed 4 May 2021). (The Graph). – reference: Deng, J. et al. Imagenet: A large-scale hierarchical image database. in 2009 IEEE Conference on Computer Vision and Pattern Recognition, 248–255 (IEEE, 2009). – reference: Wang, Q., Li, R., Wang, Q. & Chen, S. Non-fungible token (NFT): Overview, evaluation, opportunities and challenges. arXiv preprintarXiv:2105.07447 (2021). – reference: WhitakerAArt and blockchain: A primer, history, and taxonomy of blockchain use cases in the ArtsArtivate20198214610.1353/artv.2019.0008 – reference: FreundYSchapireRAbeNA short introduction to boostingJ.-Japan. Soc. Artif. Intell.1999141612 – reference: BarratABarthelemyMPastor-SatorrasRVespignaniAThe architecture of complex weighted networksProc. Natl. Acad. Sci.2004101374737522004PNAS..101.3747B1:CAS:528:DC%2BD2cXis1KgtLk%3D10.1073/pnas.0400087101 – reference: EvansTMCryptokitties, cryptography, and copyrightAIPLA QJ201947219247 – reference: Team, O. API overview. (2021). https://docs.opensea.io/reference. (Accessed 4 May 2021). (OpenSea). – reference: Tepper, F. People have spent over \$1m buying virtual cats on the Ethereum blockchain. (2017). https://techcrunch.com/2017/12/03/people-have-spent-over-1m-buying-virtual-cats-on-the-ethereum-blockchain/. (Accessed 4 May 2021). (TechCrunch). – reference: ClausetAShaliziCRNewmanMEPower-law distributions in empirical dataSIAM Rev.2009516617032009SIAMR..51..661C256382910.1137/070710111 – reference: Wong, J. I. The Ethereum network is getting jammed up because people are rushing to buy cartoon cats on its blockchain. 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| Snippet | Non Fungible Tokens (NFTs) are digital assets that represent objects like art, collectible, and in-game items. They are traded online, often with... Abstract Non Fungible Tokens (NFTs) are digital assets that represent objects like art, collectible, and in-game items. They are traded online, often with... |
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| SubjectTerms | 639/766/259 639/766/530 Artists Blockchain Collectibles Digital currencies Humanities and Social Sciences Learning algorithms Machine learning multidisciplinary Popularity Sales Science Science (multidisciplinary) Trends |
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| Title | Mapping the NFT revolution: market trends, trade networks, and visual features |
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