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. Amateur traders and
retail investors comprise a significant fraction of the NFT market. Hence it is
important that researchers highlight the relevant risks involved in NFT
trading. In this paper, we attempt to uncover common fraudulent behaviors such
as wash trading that could mislead other traders. Using market data, we design
quantitative features from the network, monetary, and temporal perspectives
that are fed into K-means clustering unsupervised learning algorithm to sort
traders into groups. Lastly, we discuss 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.

本研究旨在揭示诸如流通交易等常见的欺诈行为，以帮助监管者缩小市场中坏演员的搜索范围并为投资者提供保护。使用市场数据，我们设计了从网络、货币和时间角度出发的定量特征，将其提供给 K-means 聚类无监督学习算法，并讨论了聚类结果的重要性和监管措施如何减少不良行为。