【Author】
Nadini, Matthieu; Alessandretti, Laura; Di Giacinto, Flavio; Martino, Mauro; Aiello, Luca Maria; Baronchelli, Andrea
【Source】SCIENTIFIC REPORTS
【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.
【标题】绘制NFT革命图：市场趋势、贸易网络和可视化功能
【摘要】非货币代币（NFTs）是代表艺术品、收藏品和游戏内物品等对象的数字资产。它们在网上交易，通常使用加密货币，通常在区块链的智能合约中编码。公众对NFT的关注在2021年爆发了，当时他们的市场经历了创纪录的销售，但对其市场的整体结构和演变却知之甚少。在这里，我们分析了2017年6月23日至2021年4月27日期间有关470万个NFT的610万次交易的数据，这些数据主要从以太坊和WAX区块链上获得。首先，我们表征了市场的统计属性。第二，我们建立了互动网络，表明交易者通常专注于与类似对象相关的NFT，并与交换同类对象的其他交易者形成紧密集群。第三，我们根据物体的视觉特征对与NFTs相关的物体进行聚类，并表明集合包含视觉上同质的物体。最后，我们使用简单的机器学习算法研究了NFT销售的可预测性，发现销售历史和视觉特征是价格的良好预测因素。我们预计，这些发现将激发对不同背景下的NFT生产、采用和交易的进一步研究。
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