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Description:

UGFraud 0.1.1.3

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 bdscsafegraph@gmail.com 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 own data into a graph (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 detial, 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
Paper
Venue
Reference

SpEagle
Collective Opinion Spam Detection: Bridging Review Networks and Metadata
KDD 2015
BibTex

GANG
GANG: Detecting Fraudulent Users in Online Social Networks via Guilt-by-Association on Directed Graph
ICDM 2017
BibTex

fBox
Spotting Suspicious Link Behavior with fBox: An Adversarial Perspective
ICDM 2014
BibTex

Fraudar
FRAUDAR: Bounding Graph Fraud in the Face of Camouflage
KDD 2016
BibTex

ZooBP
ZooBP: Belief Propagation for Heterogeneous Networks
VLDB 2017
BibTex

SVD
Singular value decomposition and least squares solutions
-
BibTex

Prior
Evaluating suspicioueness based on prior information
-
-

Model Comparison

Model
Application
Graph Type
Model Type

SpEagle
Review Spam
Tripartite
MRF

GANG
Social Sybil
Bipartite
MRF

fBox
Social Fraudster
Bipartite
SVD

Fraudar
Social Fraudster
Bipartite
Dense-block

ZooBP
E-commerce Fraud
Tripartite
MRF

SVD
Dimension Reduction
Bipartite
SVD

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 bdscsafegraph@gmail.com for inquiry.