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: 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--- 许可证：MIT协议 任务类别： - 图机器学习（Graph ML） - 表格分类（Tabular Classification） 数据集展示名称：Elliptic比特币数据集 标签： - 比特币（Bitcoin） - 欺诈检测（Fraud Detection） - 图神经网络（Graph Neural Networks） - 加密货币（Cryptocurrency） 规模区间： - 10万 < 样本量 < 100万 --- # Elliptic比特币数据集 ## 数据集描述 本数据集为面向加密货币（Cryptocurrency）网络非法交易检测任务的Elliptic比特币数据集，包含标注了非法与合法交易类别的比特币交易数据。 ### 数据集结构 本数据集包含三个CSV格式文件： 1. **elliptic_txs_features.csv**：交易特征文件（每笔交易包含166个特征） - 94个本地特征：源自交易自身的信息 - 72个聚合特征：源自交易的一阶邻居节点信息 2. **elliptic_txs_classes.csv**：交易标签文件 - `1` 表示非法交易（如勒索软件、诈骗等场景） - `2` 表示合法交易（如交易所、服务平台等场景） - `unknown` 表示未标注交易 3. **elliptic_txs_edgelist.csv**：交易图边列表文件 - 有向边代表交易间的比特币资金流向 ### 数据集统计信息 - **总交易数**：203,769笔 - **标注非法交易**：约4,545笔 - **标注合法交易**：约42,019笔 - **未标注交易**：约157,205笔 - **时间步**：共49个，代表不同的交易时间段 ### 引用规范 若您使用本数据集，请引用以下原始论文： 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} } ### 使用示例 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") ### 许可证 本数据集采用MIT协议发布。 ### 补充说明 - **原始来源**：[Elliptic数据集](https://www.kaggle.com/ellipticco/elliptic-data-set) - **任务类型**：二元分类任务（非法交易 vs 合法交易） - **适配模型**：图神经网络（Graph Neural Networks）、长短期记忆网络（LSTM）、传统机器学习模型