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README.md

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+---
+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