Table of Contents

• Introduction
• Get Started
  • 1. Env setup
  • 2. Feature store setup
  • 3. Detector: Training and testing
  • 4. Explainer
  • 5. Explainer quantitative analysis (top k hit rate)
  • Supplement material
    • 6. Centrality measures on 41 communities
    • 7. Learning the hybrid explainer
    • 8. Visualizing the communities
• Data files
  • Note
• License Information
• Reference

Introduction

This paper is accepted by VLDB 2022, and is published in Vol. 15, Issue 3.

xFraud is an explainable Fraud transaction prediction system. xFraud is composed of a predictor which learns expressive representations for malicious transaction detection from the heterogeneous transaction graph via a self-attentive heterogeneous graph neural network, and an explainer that generates meaningful and human understandable explanations from graphs to facilitate further process in business unit.

Get Started

1. Env setup

Setup the python environment with conda and install pytorch and its dependencies. Notice for the pytorch related package, you may install the correct version to fit your cuda device. In the following experiment scripts, I will use the cuda10.1 version.

bash ./scripts/install-env-publish.sh

Third Party Packages (Optional)

If you are going to try the code of pyHGT in this project, please run

bash ./scripts/add-submodules.sh

to include pyHGT as submodules

2. Feature store setup

This step is to prevent the OOM issue for loading large feature data. LevelDB is used to store node features.
For this data sample, to generate the data store is not a necessary step, as we also provide the feature store (./data/feat_store_publish.db) used in the subsequent scripts.

bash ./scripts/setup-feature-store.py

3. Detector: Training and testing

bash ./scripts/run-detector.sh

Commonly used parameters are listed in the shell script.

4. Explainer

python ./xfraud/run_explainer.py

5. Explainer quantitative analysis (top k hit rate)

python ./xfraud/explainer-eval-hitrate/ours.py
python ./xfraud/explainer-eval-hitrate/random-baseline.py

Supplement material:

6. Centrality measures on 41 communities

cd ./xfraud/supplement/06Centrality_measures/
python edge_betweenness.py --type-centrality 'edge_betweenness_centrality' 
python line_graph_node_centrality.py --type-centrality 'degree'

7. Learning the hybrid explainer

cd ./xfraud/supplement/07Learning_hybrid/

# ridge
learn_equation-ridge.ipynb

# grid search with user defined A
python ours_learn-grid-A.py

# polynomial
learn_equation-polynomial.ipynb

# load the learned parameters and get the hybrid explainer weighs
python ours-learn.py

8. Visualizing the communities

cd ./xfraud/supplement/08Vis/

plot_community_prettify-hybrid.ipynb

Data files

We provide a small sample of the transaction graph and features in ./data.
We also provide the sample, its annotations, and evaluation results (in ./xfraud/explainer-eval-hitrate) we describe in the section about explainer.
All the datafiles are described in the scripts that utilize them.

Note

The data we use in the paper is proprietary, i.e., real-world transaction records on the eBay platform. We can share our eBay-small dataset (desensitized transaction records) after signing a data sharing agreement provided by eBay and approved by legal team in eBay. The data can only be shared for a legitimate, non-commercial purpose. Please email "zitzhang@ebay.com" with title "xFraud data request" and provide your name, address, email address, and we will help you submit the request.

License Information

Copyright 2020-2021 eBay Inc.

Author/Developer: Susie Xi Rao, Shuai Zhang, Zhichao Han, Zitao Zhang, Wei Min, Zhiyao Chen, Yinan Shan, Yang Zhao, Ce Zhang

Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at
https://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.

Reference

• pyHGT is from the original HGT implementation with MIT license, see https://github.com/acbull/pyHGT/tree/master/pyHGT.

Other python package licenses are listed below:

 Name                    Version          License

 dill                    0.3.0            BSD License
 fire                    0.3.1            Apache Software License
 networkx                2.4              BSD License
 pandas                  0.24.2           BSD
 plyvel                  1.3.0            BSD License 
 py4j                    0.10.7           BSD License
 pyarrow                 0.15.1           Apache Software License
 scikit-learn            0.22.2.post1     new BSD
 scipy                   1.4.1            BSD License
 seaborn                 0.9.0            BSD License
 torch                   1.5.0            BSD License
 torch-cluster           1.5.7            MIT
 torch-geometric         1.5.0            MIT
 torch-scatter           2.0.5            MIT
 torch-sparse            0.6.7            MIT
 torch-spline-conv       1.2.0            MIT
 torchvision             0.6.0a0+82fd1c8  BSD
 tqdm                    4.56.0           MIT License, Mozilla Public License 2.0 (MPL 2.0)

Citation

@article{rao2021xfraud,
  title={xFraud: Explainable Fraud Transaction Detection},
  author={Rao, Susie Xi and Zhang, Shuai and Han, Zhichao and Zhang, Zitao and Min, Wei and Chen, Zhiyao and Shan, Yinan and Zhao, Yang and Zhang, Ce},
  journal={VLDB},
  volumn={15},
  Issue={3},
  year={2022}, 
  howpublished = {\url{https://github.com/eBay/xFraud}},
  doi = {10.14778/3494124.3494128},
}