Datasets:

Zinteck
/

MELT

license: cc-by-nc-4.0
pretty_name: 'MELT: Memecoin Launch Trace'
language:
  - en
tags:
  - blockchain
  - solana
  - memecoin
  - fraud-detection
  - anomaly-detection
  - behavioral-traces
task_categories:
  - tabular-classification
size_categories:
  - 100M<n<1B
configs:
  - config_name: memecoin
    data_files: memecoin/*.parquet
  - config_name: pre_migration
    data_files: transaction/pre_migration-*.parquet
  - config_name: post_migration
    data_files: transaction/post_migration-*.parquet
  - config_name: bundle
    data_files: bundle/*.parquet
  - config_name: label
    data_files: label/*.parquet
  - config_name: feat
    data_files: feat/feature.parquet

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

MELT (MEmecoin Launch Trace) is the first behavioral-trace dataset for analyzing and detecting high-risk memecoin launches on Solana. It covers 41,470 memecoin launches issued through Pump.fun, with 200M+ on-chain transactions parsed into typed behavioral records, coordinated-account (bundle) traces, 122 engineered features, and configurable risk-level annotations.

Dataset structure

The dataset is organized into six loadable configs:

Config	Rows	Description
`memecoin`	41,470	One row per memecoin launch (metadata + timing).
`pre_migration`	~30.8M	Pre-migration (bonding-curve) transactions, typed.
`post_migration`	~187.7M	Post-migration (DEX) transactions, first hour after migration.
`bundle`	~3.33M	Coordinated-behavior traces (Co-purchase / Fund-flow / Jito Bundle) linking an entity to a shared identifier.
`label`	41,470	Risk-level annotation per memecoin.
`feat`	41,470	122 behavioral features per memecoin (five groups).

memecoin, label, and feat share the same 41,470-memecoin population and can be joined on the mint address.

`memecoin`

mint_addr, creation_time, migrate_time, name, symbol, description, image_url, twitter, website, telegram, signature, creator. Times are UNIX seconds.

`pre_migration` / `post_migration` (transactions)

mint, type (mint&swap / swap / transfer / zero), timestamp, signature, trader_map (JSON string: {account: token_change}), token_amount, sol_amount, fee_amount, price, block_slot, block_index.

`bundle`

entity, entity_type, identifier, source. A single table of coordinated-behavior traces across three sources; entities sharing an identifier within the same source are treated as one coordinated group. The entity_type field distinguishes what entity holds per source:

`source`	`entity_type`	`entity`	`identifier`
`Co-purchase`	`account`	trader account	co-purchase transaction signature
`Fund-flow`	`account`	trader account	common funder account
`Jito Bundle`	`signature`	transaction signature	Jito bundle id

For the Jito Bundle source, transactions sharing a bundle_id (identifier) were submitted in the same Jito bundle.

`label`

mint_address, min_ratio, manipulated (yes / no / empty), label (high / medium / low).

`feat`

mint_address, mint_ts, and 122 features across five groups: group1_* (contextual), group2_* (holding concentration), group3_* (market activity), group4_* (bundle statistics), plus time-series fields ts / ts_len. All features are computed strictly from pre-migration data to prevent label leakage (no label or return fields are included).

Usage

from datasets import load_dataset

memecoin = load_dataset("Zinteck/MELT", "memecoin")
pre      = load_dataset("Zinteck/MELT", "pre_migration")
post     = load_dataset("Zinteck/MELT", "post_migration")
bundle   = load_dataset("Zinteck/MELT", "bundle")
label    = load_dataset("Zinteck/MELT", "label")
feat     = load_dataset("Zinteck/MELT", "feat")

# trader_map is a JSON string
import json
row = pre["train"][0]
traders = json.loads(row["trader_map"])

License

This dataset is released under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license. It is derived from public Solana on-chain data and is intended for research and non-commercial use.

Citation

This dataset accompanies a paper under review. Citation details will be added upon publication; please refer to the anonymized submission for now.

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

Dataset structure

memecoin

pre_migration / post_migration (transactions)

bundle

label

feat