Description

The Elliptic Data Set maps Bitcoin transactions to real entities belonging to licit categories (exchanges, wallet providers, miners, licit services, etc.) versus illicit ones (scams, malware, terrorist organizations, ransomware, Ponzi schemes, etc.). The task on the dataset is to classify the illicit and licit nodes in the graph.

If you make use of the Elliptic Data Set in your research, please consider citing:

[1] Elliptic, www.elliptic.co. 

[2] M. Weber, G. Domeniconi, J. Chen, D. K. I. Weidele, C. Bellei, T. Robinson, C. E. Leiserson, "Anti-Money Laundering in Bitcoin: Experimenting with Graph Convolutional Networks for Financial Forensics", KDD ’19 Workshop on Anomaly Detection in Finance, August 2019, Anchorage, AK, USA. 

Content

This anonymized data set is a transaction graph collected from the Bitcoin blockchain. A node in the graph represents a transaction, an edge can be viewed as a flow of Bitcoins between one transaction and the other. Each node has 166 features and has been labeled as being created by a "licit", "illicit" or "unknown" entity.

Nodes and edges

The graph is made of 203,769 nodes and 234,355 edges. Two percent (4,545) of the nodes are labelled class1 (illicit). Twenty-one percent (42,019) are labelled class2 (licit). The remaining transactions are not labelled with regard to licit versus illicit.

Features

There are 166 features associated with each node. Due to intellectual property issues, we cannot provide an exact description of all the features in the dataset. There is a time step associated to each node, representing a measure of the time when a transaction was broadcasted to the Bitcoin network. The time steps, running from 1 to 49, are evenly spaced with an interval of about two weeks. Each time step contains a single connected component of transactions that appeared on the blockchain within less than three hours between each other; there are no edges connecting the different time steps.

The first 94 features represent local information about the transaction – including the time step described above, number of inputs/outputs, transaction fee, output volume and aggregated figures such as average BTC received (spent) by the inputs/outputs and average number of incoming (outgoing) transactions associated with the inputs/outputs. The remaining 72 features are aggregated features, obtained using transaction information one-hop backward/forward from the center node - giving the maximum, minimum, standard deviation and correlation coefficients of the neighbour transactions for the same information data (number of inputs/outputs, transaction fee, etc.).

License

This data set is distributed under the Creative CommonsAttribution-NonCommercial-NoDerivatives License 4.0 International.