MELT: A Behavioral Trace Dataset for High-Risk Memecoin Launch Detection

TL;DR

MELT is a comprehensive behavioral trace dataset for detecting high-risk memecoin launches on Solana, enabling improved risk analysis and mitigation through machine learning models.

Contribution

It introduces the first behavioral trace dataset for memecoin launches, including detailed transaction types, ownership structures, and risk annotations, facilitating advanced detection methods.

Findings

Coordinated accounts hold an average of 36.5% of token supply.

Behavioral features enable effective supervised learning for risk detection.

Simple investment strategies using MELT predictions reduce losses.

Abstract

Launchpads have become the dominant mechanism for issuing memecoins, exposing investors to a new class of high-risk launches that existing rug-pull detection methods cannot capture. We argue that detecting these threats requires structured behavioral traces that underlie raw heterogeneous blockchain data, i.e., how insiders accumulate, coordinate, and unwind positions. To enable such analysis, we introduce MELT (MEmecoin Launch Trace, the first behavioral trace dataset for analyzing and detecting high-risk memecoin launches on Solana. MELT covers 41k+ memecoin launches with 200M+ transactions parsed into typed behavioral records that distinguish swaps, wash trades, transfers, and mints. Beyond per-account behaviors, MELT contributes bundle-trace data that links accounts controlled by the same entity, revealing that, on average, 36.5% of token supply is held by coordinated accounts, a concealment strategy that disguises the true ownership concentration from unsuspecting buyers. On top of these traces, MELT provides 122 behavioral features and risk-level annotations, enabling supervised learning at a population scale. We benchmark representative ML models on the high-risk launch detection task. Integrating their predictions into a simple memecoin selection strategy reduces investment loss significantly, demonstrating that behavioral traces can be translated into risk mitigation. Our dataset and code is available at https://github.com/git-disl/MELT.