NFTVis: Visual Analysis of NFT Performance
Abstract
A non-fungible token (NFT) is a data unit stored on the blockchain. Nowadays, more and more investors and collectors (NFT traders), who participate in transactions of NFTs, have an urgent need to assess the performance of NFTs. However, there are two challenges for NFT traders when analyzing the performance of NFT. First, the current rarity models have flaws and are sometimes not convincing. In addition, NFT performance is dependent on multiple factors, such as images (high-dimensional data), history transactions (network), and market evolution (time series). It is difficult to take comprehensive consideration and analyze NFT performance efficiently. To address these challenges, we propose NFTVis, a visual analysis system that facilitates assessing individual NFT performance. A new NFT rarity model is proposed to quantify NFTs with images. Four well-coordinated views are designed to represent the various factors affecting the performance of the NFT. Finally, we evaluate the usefulness and effectiveness of our system using two case studies and user studies.
Cite
@article{arxiv.2306.02712,
title = {NFTVis: Visual Analysis of NFT Performance},
author = {Fan Yan and Xumeng Wang and Ketian Mao and Wei Zhang and Wei Chen},
journal= {arXiv preprint arXiv:2306.02712},
year = {2023}
}
Comments
This manuscript is accepted for publication in Proceedings of the 16th IEEE Pacific Visualization Symposium (PacificVis '23)