mlsanityData to 14 Oct 2026

Mapping the NFT revolution: market trends, trade networks and visual features

arXiv 2106.00647 ↗First posted 1 Jun 2021q-fin.ST cs.CY physics.soc-phcited by 24

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Abstract (arXiv)

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.

Key facts. Cited by 24 papers in the map (data through 14 Oct 2026). First posted on arXiv 1 Jun 2021.

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Citing papers per year: 2021: 1; 2022: 6; 2023: 11; 2024: 3; 2025: 3.

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  1. 2021When Creators Meet the Metaverse: A Survey on Computational Artscited by 12
  2. 2023Live Graph Lab: Towards Open, Dynamic and Real Transaction Graphs with NFTcited by 10
  3. 2022Compression ensembles quantify aesthetic complexity and the evolution of visual artcited by 9
  4. 2023Learning Profitable NFT Image Diffusions via Multiple Visual-Policy Guided Reinforcement Learningcited by 7
  5. 2023Show me your NFT and I tell you how it will perform: Multimodal representation learning for NFT selling price predictioncited by 6
  6. 2023EX-Graph: A Pioneering Dataset Bridging Ethereum and Xcited by 5
  7. 2023Blockchain-Empowered Trustworthy Data Sharing: Fundamentals, Applications, and Challengescited by 3
  8. 2023The Metaverse: Survey, Trends, Novel Pipeline Ecosystem & Future Directionscited by 3
  9. 2022NFT Appraisal Prediction: Utilizing Search Trends, Public Market Data, Linear Regression and Recurrent Neural Networkscited by 2
  10. 2023Exploring Gender and Race Biases in the NFT Marketcited by 1
  11. 2023NFTVis: Visual Analysis of NFT Performancecited by 1
  12. 2024Exploring the Decentraland Economy: Multifaceted Parcel Attributes, Key Insights, and Benchmarkingcited by 1
12 more
  1. 2025DMind Benchmark: Toward a Holistic Assessment of LLM Capabilities across the Web3 Domaincited by 1
  2. 2025UEChecker: Detecting Unchecked External Call Vulnerabilities in DApps via Graph Analysiscited by 1
  3. 2022Digital Asset Valuation: A Study on Domain Names, Email Addresses, and NFTscited by 0
  4. 2022The Art NFTs and Their Marketplacescited by 0
  5. 2022Projecting Non-Fungible Token (NFT) Collections: A Contextual Generative Approachcited by 0
  6. 2022Need of 6G for the Metaverse Realizationcited by 0
  7. 2023Visually Wired NFTs: Exploring the Role of Inspiration in Non-Fungible Tokenscited by 0
  8. 2023Abnormal Trading Detection in the NFT Marketcited by 0
  9. 2023What Determines the Price of NFTs?cited by 0
  10. 2024The Dual Impact of Virtual Reality: Examining the Addictive Potential and Therapeutic Applications of Immersive Media in the Metaversecited by 0
  11. 2024Fractal Signatures: Securing AI-Generated Pollock-Style Art via Intrinsic Watermarking and Blockchaincited by 0
  12. 2025DIT: Dimension Reduction View on Optimal NFT Rarity Meterscited by 0