Datasets:

yhoma
/

elliptic-bitcoin-dataset

license: mit
task_categories:
  - graph-ml
  - tabular-classification
pretty_name: Elliptic Bitcoin Dataset
tags:
  - bitcoin
  - fraud-detection
  - graph-neural-networks
  - cryptocurrency
size_categories:
  - 100K<n<1M

Elliptic Bitcoin Dataset

Dataset Description

This is the Elliptic Bitcoin dataset used for illicit transaction detection in cryptocurrency networks. The dataset contains Bitcoin transaction data with labeled illicit and licit transactions.

Dataset Structure

The dataset consists of three CSV files:

elliptic_txs_features.csv: Transaction features (166 features per transaction)
- 94 local features (derived from transaction information)
- 72 aggregated features (derived from one-hop neighbors)
elliptic_txs_classes.csv: Transaction labels
- 1 = illicit (ransomware, scam, etc.)
- 2 = licit (exchanges, services, etc.)
- unknown = unlabeled transactions
elliptic_txs_edgelist.csv: Transaction graph edges
- Directed edges representing Bitcoin flows between transactions

Dataset Statistics

Total transactions: 203,769
Labeled illicit: ~4,545 transactions
Labeled licit: ~42,019 transactions
Unlabeled: ~157,205 transactions
Time steps: 49 (representing different time periods)

Citation

If you use this dataset, please cite the original paper:

@inproceedings{weber2019anti,
  title={Anti-money laundering in bitcoin: Experimenting with graph convolutional networks for financial forensics},
  author={Weber, Mark and Domeniconi, Giacomo and Chen, Jie and Weidele, Daniel Karl I and Bellei, Claudio and Robinson, Tom and Leiserson, Charles E},
  booktitle={Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining},
  pages={1954--1964},
  year={2019}
}

Usage

from datasets import load_dataset

# Load the dataset
dataset = load_dataset("yhoma/elliptic-bitcoin-dataset")

# Access the CSV files
features_df = pd.read_csv("hf://datasets/yhoma/elliptic-bitcoin-dataset/elliptic_txs_features.csv", header=None)
classes_df = pd.read_csv("hf://datasets/yhoma/elliptic-bitcoin-dataset/elliptic_txs_classes.csv")
edges_df = pd.read_csv("hf://datasets/yhoma/elliptic-bitcoin-dataset/elliptic_txs_edgelist.csv")

License

This dataset is released under the MIT License.

Additional Information

Original Source: Elliptic Data Set
Task: Binary classification (illicit vs. licit transactions)
Suitable for: Graph Neural Networks, LSTM, traditional ML models