Identifying Washtrading Cases in NFT Sales Networks

Nargess Tahmasbi, Guohou Shan, Aaron M. French

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


Washtrading is a serious issue prevailing in nonfungible token (NFT) markets. Most current attempts to identify washtrading incidents use graph methods on transaction graphs to identify cyclical patterns. However, the intensive computational requirements hinder the practical applicability of the methods, and the single-level identification limits our view of the overall scope of the issue. In this article, we discuss the unique challenges of identifying washtrading scenarios in NFT markets and propose a solution that finds closed cycles in transaction graphs to identify potential washtrading cases at four different levels. Our identification method provides a measure of washtrading prevalence in the Nifty Gateway (NG) NFT market with approaches for evaluation that relay a measure of reliability that can be applied in other NFT markets. Acknowledging the newness of washtrading issues in NFT markets, we hope that the current study opens doors for new attempts to further investigate the problem and provide efficient and effective solutions to combat the issue.

Original languageEnglish (US)
Pages (from-to)1696-1707
Number of pages12
JournalIEEE Transactions on Computational Social Systems
Issue number2
StatePublished - Apr 1 2024

All Science Journal Classification (ASJC) codes

  • Modeling and Simulation
  • Social Sciences (miscellaneous)
  • Human-Computer Interaction

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