DualFraud

Repo Structure

The repository is organized as follows:

• dataset/: dataset folder
  • synthetic_company: Data of the synthetic company;
  • synthetic_transaction: Data of the synthetic transaction;
  • real_company: Data of the real-world company;
  • real_transaction: Data of the real-world transaction;
• model/: model folder
  • DualFraud.ipynb: data, model and training/testing code for DualFraud and DualFraud-S;
• utils/: functions folder
  • synthetic_structsim.py: generate structures for the synthetic dataset;
  • featgen.py: generate features for nodes in the synthetic dataset;

Detailed structure in DualFraud.ipynb:

• Set up: required python packages

• Data: Data generation and process

  • Synthetic Data: Synthetic data generation;
  • Real-World Data: Data process for real-world data;

• Model: Model and train/test code

  • Parameters: parameters of the model;
  • Layer: Layer definition including GraphConvLayer, MLPAttentionNetwork and BiLSTMAttentionNetwork;
  • Model: Model definition of DualFraud;
  • GNNExplainer: GNNExplainer model from Pytorch geometric;
  • DualFraud: Train/Test: Training and testing code for DualFraud;
  • DualFraud-S: Train/Test: Training and testing code for DualFraud-S;
  • Explainer: explainer component to generate explainations for enterprises and transations.

Dataset

We build Synthetic and Real-world datasets for experiments:

	Nodes(Fraud%)	#Features	Relation	#Edges
Synthetic Enterprise	1143(10.5%)	8	C-C	1181
Synthetic Transaction	4575(10.5%)	8	T-T	4749
Real-world Enterprise(HAT)	13489(26.4%)	89	C-I-C C-C C-M-C ALL	53874 15908 139413 209195
Real-world Transaction(BankSim)	50000(1.2%)	23	T-A-T	206666

How to Run

You can download the project and and run the program as follows:

1. Unzip datasets in the dataset folder \dataset
2. Install the required packages using the requirements.txt;

pip install -r requirements.txt

3. Run code in DualFraud.ipynb to run DualFraud.

* To run the code, you need to have at least Python 3.6 or later versions.

Run on your Datasets

To run DualFraud on your datasets, you need to prepare the following data for enterprises and transactions separately:

• Multiple-single relation graphs with the same nodes where each graph is stored in adjacency matrix format;
• An array with node labels;
• A node feature matrix.