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--- |
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license: mit |
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task_categories: |
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- graph-ml |
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- tabular-classification |
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pretty_name: Elliptic Bitcoin Dataset |
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tags: |
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- bitcoin |
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- fraud-detection |
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- graph-neural-networks |
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- cryptocurrency |
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size_categories: |
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- 100K<n<1M |
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--- |
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# Elliptic Bitcoin Dataset |
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## Dataset Description |
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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. |
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### Dataset Structure |
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The dataset consists of three CSV files: |
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1. **elliptic_txs_features.csv**: Transaction features (166 features per transaction) |
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- 94 local features (derived from transaction information) |
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- 72 aggregated features (derived from one-hop neighbors) |
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2. **elliptic_txs_classes.csv**: Transaction labels |
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- `1` = illicit (ransomware, scam, etc.) |
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- `2` = licit (exchanges, services, etc.) |
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- `unknown` = unlabeled transactions |
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3. **elliptic_txs_edgelist.csv**: Transaction graph edges |
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- Directed edges representing Bitcoin flows between transactions |
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### Dataset Statistics |
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- **Total transactions**: 203,769 |
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- **Labeled illicit**: ~4,545 transactions |
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- **Labeled licit**: ~42,019 transactions |
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- **Unlabeled**: ~157,205 transactions |
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- **Time steps**: 49 (representing different time periods) |
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### Citation |
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If you use this dataset, please cite the original paper: |
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```bibtex |
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@inproceedings{weber2019anti, |
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title={Anti-money laundering in bitcoin: Experimenting with graph convolutional networks for financial forensics}, |
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author={Weber, Mark and Domeniconi, Giacomo and Chen, Jie and Weidele, Daniel Karl I and Bellei, Claudio and Robinson, Tom and Leiserson, Charles E}, |
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booktitle={Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining}, |
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pages={1954--1964}, |
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year={2019} |
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} |
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``` |
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### Usage |
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```python |
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from datasets import load_dataset |
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# Load the dataset |
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dataset = load_dataset("yhoma/elliptic-bitcoin-dataset") |
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# Access the CSV files |
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features_df = pd.read_csv("hf://datasets/yhoma/elliptic-bitcoin-dataset/elliptic_txs_features.csv", header=None) |
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classes_df = pd.read_csv("hf://datasets/yhoma/elliptic-bitcoin-dataset/elliptic_txs_classes.csv") |
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edges_df = pd.read_csv("hf://datasets/yhoma/elliptic-bitcoin-dataset/elliptic_txs_edgelist.csv") |
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``` |
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### License |
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This dataset is released under the MIT License. |
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### Additional Information |
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- **Original Source**: [Elliptic Data Set](https://www.kaggle.com/ellipticco/elliptic-data-set) |
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- **Task**: Binary classification (illicit vs. licit transactions) |
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- **Suitable for**: Graph Neural Networks, LSTM, traditional ML models |
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