MemeTrans: A Dataset for Detecting High-Risk Memecoin Launches on Solana

• URL: http://arxiv.org/abs/2602.13480v1
• Date: Fri, 13 Feb 2026 21:35:15 GMT
• Title: MemeTrans: A Dataset for Detecting High-Risk Memecoin Launches on Solana
• Authors: Sihao Hu, Selim Furkan Tekin, Yichang Xu, Ling Liu,
• Abstract summary: We introduce MemeTrans, the first dataset for studying and detecting high-risk memecoin launches on Solana.<n>To precisely capture launch patterns, we design 122 features spanning dimensions such as context, trading activity, holding concentration, and time-series dynamics.<n>Experiments on the introduced high-risk launch detection task suggest that designed features are informative for capturing high-risk patterns and ML models trained on MemeTrans can effectively reduce financial loss by 56.1%.
• Score: 12.516642041569467
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Launchpads have become the dominant mechanism for issuing memecoins on blockchains due to their fully automated, no-code creation process. This new issuance paradigm has led to a surge in high-risk token launches, causing substantial financial losses for unsuspecting buyers. In this paper, we introduce MemeTrans, the first dataset for studying and detecting high-risk memecoin launches on Solana. MemeTrans covers over 40k memecoin launches that successfully migrated to the public Decentralized Exchange (DEX), with over 30 million transactions during the initial sale on launchpad and 180 million transactions after migration. To precisely capture launch patterns, we design 122 features spanning dimensions such as context, trading activity, holding concentration, and time-series dynamics, supplemented with bundle-level data that reveals multiple accounts controlled by the same entity. Finally, we introduce an annotation approach to label the risk level of memecoin launches, which combines statistical indicators with a manipulation-pattern detector. Experiments on the introduced high-risk launch detection task suggest that designed features are informative for capturing high-risk patterns and ML models trained on MemeTrans can effectively reduce financial loss by 56.1%. Our dataset, experimental code, and pipeline are publicly available at: https://github.com/git-disl/MemeTrans.

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