Heavy-tailed DecentraPunks-Exploring the Structure of NFT Sales Networks

Non-fungible Tokens (NFT) have received increased attention since 2021. The availability of the vast amount of public sales transaction data has created an unprecedented opportunity that calls for research to uncover the underlying mechanism in which NFT networks evolve. Our main goal is to understand the new space of NFT-based crypto art exchange and the structure of the trading network. We use data from the Crypto Punks collection and perform a data-driven quantitative study based on real-time trading and sales data to carry out a two-folded methodological approach that is first applied to this domain. We borrow the citation analysis and social network analysis from bibliometrics and the social network domain and apply them to the NFT space to explore the trading network structure. We found that despite being based on unique and noninterchangeable tokens, the NFT-based CryptoPunks transactions network follows the scale-free network structure, the similar pattern that is observed in Web 2.0 social networks and cryptocurrency transaction networks, where a few actors have dominant centrality. Our study demonstrates the applicability of the two approaches from bibliometrics and social network analysis to the context of unique digital assets trading.