damioncheng / dgfraud

A Deep Graph-based Toolbox for Fraud Detection

A Deep Graph-based Toolbox for Fraud Detection

Introduction: DGFraud is a Graph Neural Network (GNN) based toolbox for fraud detection. It integrates the implementation & comparison of state-of-the-art GNN-based fraud detection models. It also includes several utility functions such as graph preprocessing, graph sampling, and performance evaluation. The introduction of implemented models can be found here.

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{liu2020alleviating,
  title={Alleviating the Inconsistency Problem of Applying Graph Neural Network to Fraud Detection},
  author={Liu, Zhiwei and Dou, Yingtong and Yu, Philip S. and Deng, Yutong and Peng, Hao},
  booktitle={Proceedings of the 43nd International ACM SIGIR Conference on Research and Development in Information Retrieval},
  year={2020}
}

Useful Resources

• 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

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

Requirements

* python 3.6, 3.7
* tensorflow>=1.14.0,<2.0
* numpy>=1.16.4
* scipy>=1.2.0

Dataset

DBLP

We 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.zip

Example dataset

We 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.

User Guide

Running the example code

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.

Running on your datasets

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 structure of code

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).

Implemented Models

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

Model Comparison

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

TODO List

• GraphConsis Implementation
• Add preprocessed Yelp datasets
• The memory-efficient implementation of SemiGNN
• The log loss for GEM model
• Time-based sampling for GEM
• Add sampling methods
• Benchmarking SOTA models
• Scalable implementation
• TensorFlow 2.0+ implementation
• Pytorch version

How to Contribute

You are welcomed to contribute to this open-source toolbox. The detailed instructions will be released soon. Currently, you can create issues or send email to [email protected] for enquiry.