Welcome to the Open Fraud Detection Kit project. This project is aimed at providing an open-source solution for managing and updating fraud detection AI models and toolkit.
Author: Tianyi Lu
These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.
What things you need to install the software and how to install them:
- You need Python < 3.11 (We used 3.7. Guide to build older versions of Python
- Clone the repo
git clone - Download model from google drive name it model.pkl and put it the root of the project
- Install libraries for the project from requirements.txt
pip install -r requirements.txt - Run Flask server
python app.py - Finish!
This section is for providing examples of how your project can be used.
- Navigate to the Macros directory to access the Alteryx macros.
- Use the macros in your Alteryx workflows as necessary.
- The 'Samples' directory provides examples of how to use the macros.
- The 'TestMacros' directory contains macros that are used for testing purposes.
Please read CONTRIBUTING.md for details on our code of conduct, and the process for submitting pull requests to us.
We use SemVer for versioning. For the versions available, see the tags on this repository.
- Thanks to Alteryx for providing the platform that this project is built upon.
- All contributors who have helped to evolve this project.
Please note that this is a basic structure for a README file. Depending on your project, you might want to add more sections, for example, about the project's structure, background, or the technology stack used.
python setup.py install* python 3.6, 3.7
* tensorflow>=1.14.0,<2.0
* numpy>=1.16.4
* scipy>=1.2.0
* networkx<=1.11We uses the pre-processed DBLP dataset from Jhy1993/HAN You can run the FdGars, Player2Vec, GeniePath and GEM based on the DBLP dataset. Unzip the archive before using the dataset:
cd dataset
unzip DBLP4057_GAT_with_idx_tra200_val_800.zipWe implement example graphs for SemiGNN, GAS and GEM in data_loader.py. Because those models require unique graph structures or node types, which cannot be found in opensource datasets.
For GraphConsis, we preprocessed Yelp Spam Review Dataset with reviews as nodes and three relations as edges.
The dataset with .mat format is located at /dataset/YelpChi.zip. The .mat file includes:
net_rur, net_rtr, net_rsr: three sparse matrices representing three homo-graphs defined in GraphConsis paper;features: a sparse matrix of 32-dimension handcrafted features;label: a numpy array with the ground truth of nodes.1represents spam and0represents benign.
The YelpChi data preprocessing details can be found in our CIKM'20 paper. To get the complete metadata of the Yelp dataset, please email to ytongdou@gmail.com for inquiry.
You can find the implemented models in algorithms directory. For example, you can run Player2Vec using:
python Player2Vec_main.py You can specify parameters for models when running the code.
Have a look at the load_data_dblp() function in utils/utils.py for an example.
In order to use your own data, you have to provide:
- adjacency matrices or adjlists (for GAS);
- a feature matrix
- a label matrix then split feature matrix and label matrix into testing data and training data.
You can specify a dataset as follows:
python xx_main.py --dataset your_dataset or by editing xx_main.py
The repository is organized as follows:
algorithms/contains the implemented models and the corresponding example code;base_models/contains the basic models (GCN);dataset/contains the necessary dataset files;utils/contains:- loading and splitting the data (
data_loader.py); - contains various utilities (
utils.py).
- loading and splitting the data (
| Model | Paper | Venue | Reference |
|---|---|---|---|
| SemiGNN | A Semi-supervised Graph Attentive Network for Financial Fraud Detection | ICDM 2019 | BibTex |
| Player2Vec | Key Player Identification in Underground Forums over Attributed Heterogeneous Information Network Embedding Framework | CIKM 2019 | BibTex |
| GAS | Spam Review Detection with Graph Convolutional Networks | CIKM 2019 | BibTex |
| FdGars | FdGars: Fraudster Detection via Graph Convolutional Networks in Online App Review System | WWW 2019 | BibTex |
| GeniePath | GeniePath: Graph Neural Networks with Adaptive Receptive Paths | AAAI 2019 | BibTex |
| GEM | Heterogeneous Graph Neural Networks for Malicious Account Detection | CIKM 2018 | BibTex |
| GraphSAGE | Inductive Representation Learning on Large Graphs | NIPS 2017 | BibTex |
| GraphConsis | Alleviating the Inconsistency Problem of Applying Graph Neural Network to Fraud Detection | SIGIR 2020 | BibTex |
| HACUD | Cash-Out User Detection Based on Attributed Heterogeneous Information Network with a Hierarchical Attention Mechanism | AAAI 2019 | BibTex |
| Model | Application | Graph Type | Base Model |
|---|---|---|---|
| SemiGNN | Financial Fraud | Heterogeneous | GAT, LINE, DeepWalk |
| Player2Vec | Cyber Criminal | Heterogeneous | GAT, GCN |
| GAS | Opinion Fraud | Heterogeneous | GCN, GAT |
| FdGars | Opinion Fraud | Homogeneous | GCN |
| GeniePath | Financial Fraud | Homogeneous | GAT |
| GEM | Financial Fraud | Heterogeneous | GCN |
| GraphSAGE | Opinion Fraud | Homogeneous | GraphSAGE |
| GraphConsis | Opinion Fraud | Heterogeneous | GraphSAGE |
| HACUD | Financial Fraud | Heterogeneous | GAT |