Tommy-DING/crypto-illicit-account-detection-multigraphs

Crypto Illicit Account Detection Multigraphs (DIAM)

This dataset repository (Tommy-DING/crypto-illicit-account-detection-multigraphs) provides four large-scale directed multigraph cryptocurrency transaction networks used in:

Effective Illicit Account Detection on Large Directed MultiGraph Transaction Networks of Cryptocurrencies
Zhihao Ding, Jieming Shi, Qing Li, Jiannong Cao. CIKM 2024.

The primary task is illicit account detection (node-level classification) on transaction graphs.

Official PyTorch Geometric implementation & .pt data: https://github.com/TommyDzh/DIAM

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Dataset Contents

This repo contains NumPy compressed archives (.npz):

• EthereumS_graph_dict.npz
• EthereumP_graph_dict.npz
• BitcoinM_graph_dict.npz
• BitcoinL_graph_dict.npz

Each file stores a single large transaction graph as a graph dictionary (see below).

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Data Format (.npz graph dict)

Each *_graph_dict.npz contains the following keys:

• edge_index: shape [2, E]
  Directed edge list in COO format (source nodes in row 0, destination nodes in row 1).

• edge_attr: shape [E, D]
  Edge attributes (transaction-level features).

  • Ethereum-S / Ethereum-P: D = 2 (transaction amount, timestamp)
  • Bitcoin-M: D = 5 (input amount, output amount, #inputs, #outputs, timestamp)
  • Bitcoin-L: D = 8 (input amount, output amount, #inputs, #outputs, fee, total inputs value, total outputs value, timestamp)

• X: shape [N, F]
  Node features (pre-computed / feature-engineered). The paper reports feature dimensions of:

  • Ethereum-P: F = 48
  • Ethereum-S: F = 48
  • Bitcoin-M: F = 69
  • Bitcoin-L: F = 89
    Note: DIAM itself learns node representations from edge-attribute sequences and does not require these engineered node features, but they are provided for convenience and for running feature-based baselines.

• y: shape [N]
  Node labels in {-1, 0, 1}:

  • -1: unknown / unlabeled
  • 0: benign / normal
  • 1: illicit

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Loading Example

import numpy as np

z = np.load("EthereumS_graph_dict.npz")
edge_index = z["edge_index"]  # [2, E]
edge_attr  = z["edge_attr"]   # [E, D]
X          = z["X"]           # [N, F]
y          = z["y"]           # [N]

Convert to PyTorch Geometric Data

import numpy as np
import torch
from torch_geometric.data import Data

z = np.load("EthereumS_graph_dict.npz")

data = Data(
    x=torch.from_numpy(z["X"]).float(),
    edge_index=torch.from_numpy(z["edge_index"]).long(),
    edge_attr=torch.from_numpy(z["edge_attr"]).float(),
    y=torch.from_numpy(z["y"]).long(),
)

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Datasets & Label Semantics (per paper)

The repo includes two Ethereum graphs and two Bitcoin graphs:

• Ethereum-S / Ethereum-P: illicit nodes correspond to addresses conducting phishing scams.

  • Ethereum-P originally contains illicit labels; benign labels are enhanced by identifying benign accounts (e.g., wallets / finance services) from Etherscan.

• Bitcoin-M / Bitcoin-L: illicit nodes correspond to Bitcoin addresses belonging to gambling and mixing services (strongly associated with money laundering). Other types are treated as normal.

Ground-truth labels are crawled from public sources including Etherscan and WalletExplorer, as described in the paper.

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Recommended Usage

• For the official DIAM pipeline (edge sequence generation, training, and evaluation), follow the GitHub instructions: https://github.com/TommyDzh/DIAM

• If you train your own models directly on these .npz graphs, a common practice is:

  • use nodes with y in {0,1} as labeled set,
  • ignore y == -1 during supervised loss/evaluation,
  • construct train/val/test splits according to your experimental protocol (the DIAM repo provides the reference implementation).

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Citation

If you use this dataset, please cite:

@inproceedings{ding2024effective,
  title={Effective illicit account detection on large cryptocurrency multigraphs},
  author={Ding, Zhihao and Shi, Jieming and Li, Qing and Cao, Jiannong},
  booktitle={Proceedings of the 33rd ACM International Conference on Information and Knowledge Management},
  pages={457--466},
  year={2024}
}