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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Vydané v:	Scientific reports Ročník 11; číslo 1; s. 20902 - 11
Hlavní autori:	Nadini, Matthieu, Alessandretti, Laura, Di Giacinto, Flavio, Martino, Mauro, Aiello, Luca Maria, Baronchelli, Andrea
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	London Nature Publishing Group UK 22.10.2021 Nature Publishing Group Nature Portfolio
Predmet:	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
ISSN:	2045-2322, 2045-2322
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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.
AbstractList	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. 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.
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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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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