| | --- |
| | 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: |
| |
|
| | 1. **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) |
| |
|
| | 2. **elliptic_txs_classes.csv**: Transaction labels |
| | - `1` = illicit (ransomware, scam, etc.) |
| | - `2` = licit (exchanges, services, etc.) |
| | - `unknown` = unlabeled transactions |
| |
|
| | 3. **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: |
| |
|
| | ```bibtex |
| | @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 |
| |
|
| | ```python |
| | 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](https://www.kaggle.com/ellipticco/elliptic-data-set) |
| | - **Task**: Binary classification (illicit vs. licit transactions) |
| | - **Suitable for**: Graph Neural Networks, LSTM, traditional ML models |
| |
|
