An Unsupervised Graph-based Toolbox for Fraud Detection

An Unsupervised Graph-based Toolbox for Fraud Detection

Introduction: UGFraud is an unsupervised graph-based fraud detection toolbox that integrates several state-of-the-art graph-based fraud detection algorithms. It can be applied to bipartite graphs (e.g., user-product graph), and it can estimate the suspiciousness of both nodes and edges. The implemented models can be found here.

The toolbox incorporates the Markov Random Field (MRF)-based algorithm, dense-block detection-based algorithm, and SVD-based algorithm. For MRF-based algorithms, the users only need the graph structure and the prior suspicious score of the nodes as the input. For other algorithms, the graph structure is the only input.

Meanwhile, we have a deep graph-based fraud detection toolbox which implements state-of-the-art graph neural network-based fraud detectors.

We welcome contributions on adding new fraud detectors and extending the features of the toolbox. Some of the planned features are listed in TODO list.

If you use the toolbox in your project, please cite the paper below and the algorithms you used :

@inproceedings{dou2020robust,
  title={Robust Spammer Detection by Nash Reinforcement Learning},
  author={Dou, Yingtong and Ma, Guixiang and Yu, Philip S and Xie, Sihong},
  booktitle={Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery \& Data Mining},
  year={2020}
}

Useful Resources

• DGFraud: A Deep Graph-based Fraud Detection Toolbox
• Graph-based Fraud Detection Paper List
• Awesome Fraud Detection Papers
• Attack and Defense Papers on Graph Data
• PyOD: A Python Toolbox for Scalable Outlier Detection (Anomaly Detection)
• PyODD: An End-to-end Outlier Detection System
• DGL: Deep Graph Library
• Outlier Detection DataSets (ODDS)

Table of Contents

• Installation
• User Guide
• Implemented Models
• Model Comparison
• TODO List
• How to Contribute

Installation

You can install UGFraud from pypi:

pip install UGFraud

or download and install from github:

git clone https://github.com/safe-graph/UGFraud.git
cd UGFraud
python setup.py install

Dataset

The demo data is not the intact data (rating and date information are missing). The rating information is only used in ZooBP demo. If you need the intact date to play demo, please email [email protected] to download the intact data from Yelp Spam Review Dataset. The metadata.gz file in /UGFraud/Yelp_Data/YelpChi includes:

• user_id: 38063 number of users
• product_id: 201 number of products
• rating: from 1.0 (low) to 5.0 (high)
• label: -1 is not spam, 1 is spam
• date: data creation time

User Guide

Running the example code

You can find the implemented models in /UGFraud/Demo directory. For example, you can run fBox using:

python eval_fBox.py 

Running on your datasets

Have a look at the /UGFraud/Demo/data_to_network_graph.py to convert your data into the networkx graph.

In order to use your own data, you have to provide the following information at least:

• a dict of dict:

'user_id':{
        'product_id':
                {
                'label': 1
                }

• a dict of prior

You can use dict_to networkx(graph_dict) function from /Utils/helper.py file to convert your graph_dict into a networkx graph. For more details, please see data_to_network_graph.py.

The structure of code

The /UGFraud repository is organized as follows:

• Demo/ contains the implemented models and the corresponding example code;
• Detector/ contains the basic models;
• Yelp_Data/ contains the necessary dataset files;
• Utils/ contains the every help functions.

Implemented Models

Model Comparison

TODO List

• Homogeneous graph implementation

How to Contribute

You are welcomed to contribute to this open-source toolbox. Currently, you can create issues or send email to [email protected] for inquiry.