Detect fraud in transactions graphs using various ready-to-use models and datasets.

Models:

• GAT
• GCN
• GIN
• GraphSAGE
• MPNN
• GTN

Datasets:

• Elliptic
• YelpFraud
• AmazonFraud

First install the requirements.

pip install -r requirements.txt

Download and extract the required datasets. This project currently only supports Elliptic. A dataset must have 3 files:

• Edge list
• Classes
• Features

Then provide the required torch.utils.data.Dataset class for your own dataset like here

To train a model you need a config file. By default, these files are located at configs/. For example to train a GAT model on Elliptic run:

python train --config configs/elliptic_gat.yaml

The models are trained for 100 epochs and results are verbosed every 10 epochs and logged to Tensorboard.

To visualize model predictions on the graph on each step, run:

python visualize.py --config configs/elliptic_gat.yaml --step 30 --weights_file weigths/elliptic_gat.pt

This is my final project for the course Complex Networks during my Masters' in Shahid Beheshti University instructed by Dr. Sadegh Ali Akbari (Spring 2023). I've also written a Persian blog post on this project here