MELT: A Behavioral Trace Dataset for High-Risk Memecoin Launch Detection
For investors and regulators in the cryptocurrency space, this work provides a novel dataset and method to detect high-risk memecoin launches, addressing a gap in existing rug-pull detection.
The paper introduces MELT, the first behavioral trace dataset for detecting high-risk memecoin launches on Solana, covering 41k+ launches and 200M+ transactions. Using this dataset, they show that 36.5% of token supply is held by coordinated accounts, and a simple selection strategy based on ML predictions significantly reduces investment loss.
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.