Visually Wired NFTs: Exploring the Role of Inspiration in Non-Fungible Tokens
This work provides insights into the evolution of Web3 by exploring how inspiration shapes NFT markets, though it is incremental in applying existing methods to new data.
The study analyzed visual inspiration networks among Non-Fungible Tokens (NFTs) using Vision Transformers and graph-based modeling, revealing that inspiration led to temporary saturation of visual features and influenced financial performance based on the dichotomy between inspiring and inspired NFTs.
The fervor for Non-Fungible Tokens (NFTs) attracted countless creators, leading to a Big Bang of digital assets driven by latent or explicit forms of inspiration, as in many creative processes. This work exploits Vision Transformers and graph-based modeling to delve into visual inspiration phenomena between NFTs over the years. Our goals include unveiling the main structural traits that shape visual inspiration networks, exploring the interrelation between visual inspiration and asset performances, investigating crypto influence on inspiration processes, and explaining the inspiration relationships among NFTs. Our findings unveil how the pervasiveness of inspiration led to a temporary saturation of the visual feature space, the impact of the dichotomy between inspiring and inspired NFTs on their financial performance, and an intrinsic self-regulatory mechanism between markets and inspiration waves. Our work can serve as a starting point for gaining a broader view of the evolution of Web3.