# NFTVis: Visual Analysis of NFT Performance

arXiv: https://arxiv.org/abs/2306.02712 · HTML: https://mlsanity.com/paper/2306.02712/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 59 references that are in the map.
- First posted on arXiv 5 Jun 2023.

## Builds on (most cited first, 10 of 10)

- [Non-Fungible Token (NFT): Overview, Evaluation, Opportunities and Challenges](https://mlsanity.com/paper/2105.07447/) (2021)
- [Mapping the NFT revolution: market trends, trade networks and visual features](https://mlsanity.com/paper/2106.00647/) (2021)
- [TweetBoost: Influence of Social Media on NFT Valuation](https://mlsanity.com/paper/2201.08373/) (2022)
- [Bitcoin Transaction Graph Analysis](https://mlsanity.com/paper/1502.01657/) (2015)
- [Spotting Anomalous Trades in NFT Markets: The Case of NBA Topshot](https://mlsanity.com/paper/2202.04013/) (2022)
- [sPortfolio: Stratified Visual Analysis of Stock Portfolios](https://mlsanity.com/paper/1910.05536/) (2019)
- [HITS hits art](https://mlsanity.com/paper/1907.07758/) (2019)
- [iQUANT: Interactive Quantitative Investment Using Sparse Regression Factors](https://mlsanity.com/paper/2104.11485/) (2021)
- ["It's A Blessing and A Curse": Unpacking Creators' Practices with Non-Fungible Tokens (NFTs) and Their Communities](https://mlsanity.com/paper/2201.13233/) (2022)
- [Non-fungible Tokens: Promise or Peril?](https://mlsanity.com/paper/2202.06354/) (2022)

## Built on by (most cited first, 1 of 1)

- [DIT: Dimension Reduction View on Optimal NFT Rarity Meters](https://mlsanity.com/paper/2508.12671/) (2025)

## Abstract (arXiv)

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.