torch_geometric.datasets.EllipticBitcoinTemporalDataset

class EllipticBitcoinTemporalDataset(root: str, t: int, transform: Optional[Callable] = None, pre_transform: Optional[Callable] = None, force_reload: bool = False)[source]

Bases: EllipticBitcoinDataset

The time-step aware Elliptic Bitcoin dataset of Bitcoin transactions from the “Anti-Money Laundering in Bitcoin: Experimenting with Graph Convolutional Networks for Financial Forensics” paper.

EllipticBitcoinTemporalDataset 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.)

There exists 203,769 node transactions and 234,355 directed edge payments flows, with two percent of nodes (4,545) labelled as illicit, and twenty-one percent of nodes (42,019) labelled as licit. The remaining transactions are unknown.

Note

In contrast to EllipticBitcoinDataset, this dataset returns Bitcoin transactions only for a given timestamp t.

Parameters:

  • root (str) – Root directory where the dataset should be saved.

  • t (int) – The Timestep for which nodes should be selected (from 1 to 49).

  • transform (callable, optional) – A function/transform that takes in an torch_geometric.data.Data object and returns a transformed version. The data object will be transformed before every access. (default: None)

  • pre_transform (callable, optional) – A function/transform that takes in an torch_geometric.data.Data object and returns a transformed version. The data object will be transformed before being saved to disk. (default: None)

  • force_reload (bool, optional) – Whether to re-process the dataset. (default: False)

STATS:

#nodes

#edges

#features

#classes

203,769

234,355

165

2