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Characterizing the Solana NFT Ecosystem

Dechao Kong, Xiaoqi Li, Wenkai Li

TL;DR

This work addresses the problem of limited systemic understanding of Solana NFT markets and potential manipulation. It combines a longitudinal analysis of a large Solana NFT dataset (132,736 collections and 28,706,698 sales from Solscan) with a wash-trading security audit using the Local Outlier Factor (LOF) approach on 2,175 popular NFTs. Key findings show strong top-holder concentration and market influence, with 138 NFTs exhibiting wash trading across 46,612 transactions totaling $3.74M; wash-trade activity concentrates in a few marketplaces (notably Tensor and Magic Eden) and eight collections surpass a 50% wash-trade rate. The study provides actionable insights into market integrity, offers open-source data/code, and lays groundwork for risk mitigation and regulator-facing surveillance in the Solana NFT space.

Abstract

Non-Fungible Tokens (NFTs) are digital assets recorded on the blockchain, providing cryptographic proof of ownership over digital or physical items. Although Solana has only begun to gain popularity in recent years, its NFT market has seen substantial transaction volumes. In this paper, we conduct the first systematic research on the characteristics of Solana NFTs from two perspectives: longitudinal measurement and wash trading security audit. We gathered 132,736 Solana NFT from Solscan and analyzed the sales data within these collections. Investigating users' economic activity and NFT owner information reveals that the top users in Solana NFT are skewed toward a higher distribution of purchases. Subsequently, we employ the Local Outlier Factor algorithm to conduct a wash trading audit on 2,175 popular Solana NFTs. We discovered that 138 NFT pools are involved in wash trading, with 8 of these NFTs having a wash trading rate exceeding 50%. Fortunately, none of these NFTs have been entirely washed out.

Characterizing the Solana NFT Ecosystem

TL;DR

This work addresses the problem of limited systemic understanding of Solana NFT markets and potential manipulation. It combines a longitudinal analysis of a large Solana NFT dataset (132,736 collections and 28,706,698 sales from Solscan) with a wash-trading security audit using the Local Outlier Factor (LOF) approach on 2,175 popular NFTs. Key findings show strong top-holder concentration and market influence, with 138 NFTs exhibiting wash trading across 46,612 transactions totaling $3.74M; wash-trade activity concentrates in a few marketplaces (notably Tensor and Magic Eden) and eight collections surpass a 50% wash-trade rate. The study provides actionable insights into market integrity, offers open-source data/code, and lays groundwork for risk mitigation and regulator-facing surveillance in the Solana NFT space.

Abstract

Non-Fungible Tokens (NFTs) are digital assets recorded on the blockchain, providing cryptographic proof of ownership over digital or physical items. Although Solana has only begun to gain popularity in recent years, its NFT market has seen substantial transaction volumes. In this paper, we conduct the first systematic research on the characteristics of Solana NFTs from two perspectives: longitudinal measurement and wash trading security audit. We gathered 132,736 Solana NFT from Solscan and analyzed the sales data within these collections. Investigating users' economic activity and NFT owner information reveals that the top users in Solana NFT are skewed toward a higher distribution of purchases. Subsequently, we employ the Local Outlier Factor algorithm to conduct a wash trading audit on 2,175 popular Solana NFTs. We discovered that 138 NFT pools are involved in wash trading, with 8 of these NFTs having a wash trading rate exceeding 50%. Fortunately, none of these NFTs have been entirely washed out.
Paper Structure (8 sections, 3 equations, 4 figures, 3 tables)

This paper contains 8 sections, 3 equations, 4 figures, 3 tables.

Table of Contents

  1. INTRODUCTION
  2. METHODOLOGY
  3. Datasets
  4. Data Overview
  5. MARKET MANIPULATION
  6. Wash Trade Detection
  7. Experiments and Result
  8. CONCLUSION

Figures (4)

  • Figure 1: Timeline of Buyers, Sellers, and Transaction Volume. Transactions serve as the primary axis on the left, while the number of buyers and sellers constitutes the secondary axis on the right.
  • Figure 2: Lorenz Curves Representing Cumulative Purchase Quantity for Buyer and Seller Percentiles
  • Figure 3: Distribution of Wash Trading Rates among NFT Collections
  • Figure 4: The Comparative WTR across Different Marketplaces