The Non-Fungible-Token (NFT) market has experienced explosive growth in recent years. According to DappRadar, the total transaction volume on OpenSea, the largest NFT marketplace, reached 34.7 billion dollars in February 2023. However, the NFT market is mostly unregulated and there are significant concerns about money laundering, fraud and wash trading. The lack of industry-wide regulations, and the fact that amateur traders and retail investors comprise a significant fraction of the NFT market, make this market particularly vulnerable to fraudulent activities. Therefore it is essential to investigate and highlight the relevant risks involved in NFT trading. In this paper, we attempted to uncover common fraudulent behaviors such as wash trading that could mislead other traders. Using market data, we designed quantitative features from the network, monetary, and temporal perspectives that were fed into K-means clustering unsupervised learning algorithm to sort traders into groups. Lastly, we discussed the clustering results' significance and how regulations can reduce undesired behaviors. Our work can potentially help regulators narrow down their search space for bad actors in the market as well as provide insights for amateur traders to protect themselves from unforeseen frauds.

翻译：近年来，非同质化代币（NFT）市场经历了爆发式增长。根据DappRadar的数据，最大NFT市场OpenSea在2023年2月的总交易额达到347亿美元。然而，NFT市场监管匮乏，洗钱、欺诈及虚假交易问题令人担忧。由于缺乏行业层面的监管，且业余交易者和散户投资者占NFT市场的显著比例，这使得该市场极易遭受欺诈行为侵害。因此，深入探究并揭示NFT交易中的相关风险至关重要。本文尝试揭露诸如虚假交易等可能误导其他交易者的常见欺诈行为。我们基于市场数据，从网络、货币和时间维度设计量化特征，并将其输入至K-means聚类无监督学习算法，从而对交易者进行分组。最后，我们讨论了聚类结果的意义，以及如何通过监管减少不当行为。本研究有望协助监管机构缩小市场不良行为者的排查范围，同时为业余交易者提供保护自身免受未知欺诈的见解。