Authors: Tian Qiu, Wenda Li, Zunlei Feng, Jie Lei, Tao Wang, Yi Gao, Mingli Song, Yang Gao
Affiliation: Zhejiang University
[Paper] | [Poster] | [Dataset (百度网盘)] | [Dataset (Google Drive)]

Fraudulent activities have caused substantial negative social impacts and are exhibiting emerging characteristics such as intelligence and industrialization, posing challenges of high-order interactions, intricate dependencies, and the sparse yet concealed nature of fraudulent entities. Existing graph fraud detectors are limited by their narrow "receptive fields", as they focus only on the relations between an entity and its neighbors while neglecting longer-range structural associations hidden between entities. To address this issue, we propose a novel fraud detector based on Graph Path Aggregation (GPA). It operates through variable-length path sampling, semantic-associated path encoding, path interaction and aggregation, and aggregation-enhanced fraud detection. To further facilitate interpretable association analysis, we synthesize G-Internet, the first benchmark dataset in the field of internet fraud detection. Extensive experiments across datasets in multiple fraud scenarios demonstrate that the proposed GPA outperforms mainstream fraud detectors by up to +15% in Average Precision (AP). Additionally, GPA exhibits enhanced robustness to noisy labels and provides excellent interpretability by uncovering implicit fraudulent patterns across broader contexts.

The development environment of this project is python 3.8 & pytorch 1.13.1+cu117 & dgl 1.1.3+cu117.

1. Create your conda environment.

conda create -n qtcls python==3.8 -y

2. Enter your conda environment.

conda activate qtcls

3. Install PyTorch.

pip install torch==1.13.1+cu117 torchvision==0.14.1+cu117 --extra-index-url https://download.pytorch.org/whl/cu117

Or you can refer to PyTorch to install newer or older versions. Please note that if pytorch ≥ 1.13, then python ≥ 3.7.2 is required.

4. Install DGL.

pip install dgl==1.1.3+cu117 -f https://data.dgl.ai/wheels/cu117/repo.html

(optional) Add environment variables in ~/.bashrc.

export DGLBACKEND="pytorch"

5. Install CUDA.

wget https://developer.download.nvidia.com/compute/cuda/11.7.1/local_installers/cuda_11.7.1_515.65.01_linux.run
sh cuda_11.7.1_515.65.01_linux.run

Add environment variables in ~/.bashrc.

export PATH=/path/to/cuda/bin:$PATH
export LD_LIBRARY_PATH=/path/to/cuda/lib64:$LD_LIBRARY_PATH

6. Install necessary dependencies.

pip install -r requirements.txt

1. Download graph_fraud_datasets.zip from [百度网盘] / [Google Drive] and put the file into data/raw.

2. Unzip the file.

cd data/raw
unzip graph_fraud_datasets.zip
cd ../..

Import the config file (.py) from configs.

python main.py --config /path/to/config.py

or

python main.py -c /path/to/config.py

During training, the config file, checkpoints (.pth), logs, and other outputs will be stored in --output_dir.

python main.py --config /path/to/config.py --resume /path/to/checkpoint.pth --eval

or

python main.py -c /path/to/config.py -r /path/to/checkpoint.pth --eval

Our code is released under the Apache 2.0 license. Please see the LICENSE file for more information.

Copyright (c) QIU Tian and ZJU-VIPA Lab. All rights reserved.

Licensed under the Apache License, Version 2.0 (the "License"); you may not use these files except in compliance with the License. You may obtain a copy of the License at http://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.

If you find the paper useful in your research, please consider citing:

@inproceedings{gpa,
  title={Association-Focused Path Aggregation for Graph Fraud Detection},
  author={Qiu, Tian and Li, Wenda and Feng, Zunlei and Lei, Jie and Wang, Tao and Gao, Yi and Song, Mingli and Gao, Yang},
  booktitle={Advances in Neural Information Processing Systems},
  year={2025}
}