This repository contains all models and adaptations needed to run Multi-GNN for Anti-Money Laundering. The repository consists of four Graph Neural Network model classes (GIN, GAT, PNA, RGCN) and the below-described model adaptations utilized for financial crime detection in Egressy et al.. Note that this repository solely focuses on the Anti-Money Laundering use case.

To use the repository, you first need to install the conda environment via

conda env create -f env.yml

Then, the data needed for the experiments can be found on Kaggle. To use this data with the provided training scripts, you first need to perform a pre-processing step:

python format_kaggle_files.py /path/to/kaggle-file/

Make sure to change the filepath at the beginning of the data_loading.py file to wherever you stored the formatted_transactions.csv file generated by the pre-processing step.

To run the experiments you need to run the main.py function and specify any arguments you want to use. There are two required arguments, namely --data and --model. For the --data argument, make sure you store the different datasets in different folders. Then, specify the folder name, e.g --data Small_HI. The --model parameter should be set to any of the model classed that are available, i.e. to one of --model [gin, gat, rgcn, pna]. Thus, to run a standard GNN, you need to run, e.g.:

python main.py --data Small_HI --model gin

Then you can add different adaptations to the models by selecting the respective arguments from:

python main.py --data Small_HI --model gin --emlps --reverse_mp --ego --ports

Apache License Version 2.0, January 2004