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

arXiv: https://arxiv.org/abs/2106.00647 · HTML: https://mlsanity.com/paper/2106.00647/

## Key facts

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

## Built on by (most cited first, 24 of 24)

- [When Creators Meet the Metaverse: A Survey on Computational Arts](https://mlsanity.com/paper/2111.13486/) (2021)
- [Live Graph Lab: Towards Open, Dynamic and Real Transaction Graphs with NFT](https://mlsanity.com/paper/2310.11709/) (2023)
- [Compression ensembles quantify aesthetic complexity and the evolution of visual art](https://mlsanity.com/paper/2205.10271/) (2022)
- [Learning Profitable NFT Image Diffusions via Multiple Visual-Policy Guided Reinforcement Learning](https://mlsanity.com/paper/2306.11731/) (2023)
- [Show me your NFT and I tell you how it will perform: Multimodal representation learning for NFT selling price prediction](https://mlsanity.com/paper/2302.01676/) (2023)
- [EX-Graph: A Pioneering Dataset Bridging Ethereum and X](https://mlsanity.com/paper/2310.01015/) (2023)
- [Blockchain-Empowered Trustworthy Data Sharing: Fundamentals, Applications, and Challenges](https://mlsanity.com/paper/2303.06546/) (2023)
- [The Metaverse: Survey, Trends, Novel Pipeline Ecosystem & Future Directions](https://mlsanity.com/paper/2304.09240/) (2023)
- [NFT Appraisal Prediction: Utilizing Search Trends, Public Market Data, Linear Regression and Recurrent Neural Networks](https://mlsanity.com/paper/2204.12932/) (2022)
- [Exploring Gender and Race Biases in the NFT Market](https://mlsanity.com/paper/2304.06484/) (2023)
- [NFTVis: Visual Analysis of NFT Performance](https://mlsanity.com/paper/2306.02712/) (2023)
- [Exploring the Decentraland Economy: Multifaceted Parcel Attributes, Key Insights, and Benchmarking](https://mlsanity.com/paper/2404.07533/) (2024)
- [DMind Benchmark: Toward a Holistic Assessment of LLM Capabilities across the Web3 Domain](https://mlsanity.com/paper/2504.16116/) (2025)
- [UEChecker: Detecting Unchecked External Call Vulnerabilities in DApps via Graph Analysis](https://mlsanity.com/paper/2508.01343/) (2025)
- [Digital Asset Valuation: A Study on Domain Names, Email Addresses, and NFTs](https://mlsanity.com/paper/2210.10637/) (2022)
- [The Art NFTs and Their Marketplaces](https://mlsanity.com/paper/2210.14942/) (2022)
- [Projecting Non-Fungible Token (NFT) Collections: A Contextual Generative Approach](https://mlsanity.com/paper/2210.15493/) (2022)
- [Need of 6G for the Metaverse Realization](https://mlsanity.com/paper/2301.03386/) (2022)
- [Visually Wired NFTs: Exploring the Role of Inspiration in Non-Fungible Tokens](https://mlsanity.com/paper/2303.17031/) (2023)
- [Abnormal Trading Detection in the NFT Market](https://mlsanity.com/paper/2306.04643/) (2023)
- [What Determines the Price of NFTs?](https://mlsanity.com/paper/2310.01815/) (2023)
- [The Dual Impact of Virtual Reality: Examining the Addictive Potential and Therapeutic Applications of Immersive Media in the Metaverse](https://mlsanity.com/paper/2401.03461/) (2024)
- [Fractal Signatures: Securing AI-Generated Pollock-Style Art via Intrinsic Watermarking and Blockchain](https://mlsanity.com/paper/2410.20519/) (2024)
- [DIT: Dimension Reduction View on Optimal NFT Rarity Meters](https://mlsanity.com/paper/2508.12671/) (2025)

## 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.