Goal: Train a GNN on the Elliptic Bitcoin transaction graph and deploy it in a simulated real-time system that ingests streaming transactions and flags illicit activity using a rolling subgraph.
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.).