RioGNN-sr

Effects of Scale Regularization in Fraud Detection Graphs

https://doi.org/10.3390/electronics14183660

Electronics 2025, 14(18), 3660
ISSN:2079-9292
eISSN:2079-9292

Repo Structure

The repository is organized as follows:

• data/: dataset folder
  • Amazon.zip: Data of the dataset Amazon;
• log/: log folder
• model/: model folder
  • graphsage.py: model code for vanilla GraphSAGE model;
  • layers.py: RioGNN-sr layers implementations;
  • model.py: RioGNN-sr model implementations;
• RL/: RL folder
  • actor_critic.py: RL algorithm, Actor-Critic;
  • rl_model.py: RioGNN-sr RL Forest implementations;
• utils/: functions folder
  • data_process.py: transfer sparse matrix to adjacency lists;
  • utils.py: utility functions for data i/o and model evaluation;
• train.py: training and testing all models

Example Dataset

We build different multi-relational graphs for experiments in two task scenarios and three datasets:

Dataset	Task	Nodes	Relation
Amazon	Fraud Detection	11,944	upu, usu, uvu, homo

Run on your Datasets

To run RioGNN-sr on your datasets, you need to prepare the following data:

• Multiple-single relation graphs with the same nodes where each graph is stored in scipy.sparse matrix format, you can use sparse_to_adjlist() in utils.py to transfer the sparse matrix into adjacency lists used by RioGNN-sr;
• A numpy array with node labels. Currently, RioGNN-sr only supports binary classification;
• A node feature matrix stored in scipy.sparse matrix format.

How to Run

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

1. Unzip amazon.zip in the dataset folder \data;

unzip data/amazon.zip

* Note that all datasets need to be unzipped in the folder \data first;

2. Install the required packages using the requirements.txt;

pip3 install -r requirements.txt

3. Run data_process.py to generate adjacency lists of different dataset used by RioGNN-sr;

python data_process.py

4. Run train.py to run RioGNN-sr with default settings.

python train.py --embed_rescalied

4.1. Run mecro.sh to run RioGNN-sr with default iteratively.

# '72' is the seed value
mkdir log/amazon/seed-72
mkdir log/amazon/seed-72/log
sh mecro.sh

* To run the code, you need to have Python 3.10.

Citation

If you use our code, please cite the paper below:

@article{jeon2025effects,
  title={Effects of Scale Regularization in Fraud Detection Graphs},
  author={Jeon, Janggun and Ahn, Junho and Kim, Namgi},
  journal={Electronics},
  volume={14},
  number={18},
  pages={3660},
  year={2025},
  publisher={MDPI}
}