# node1 **Repository Path**: cwjsgyh/node1 ## Basic Information - **Project Name**: node1 - **Description**: No description available - **Primary Language**: Unknown - **License**: Not specified - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 1 - **Created**: 2025-04-16 - **Last Updated**: 2025-08-31 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README # AntiFraud A Financial Fraud Detection Framework. Source codes implementation of papers: - `MCNN`: Credit card fraud detection using convolutional neural networks, published in [ICONIP 2016](https://link.springer.com/chapter/10.1007/978-3-319-46675-0_53). - `STAN`: Spatio-temporal attention-based neural network for credit card fraud detection, published in [AAAI 2020](https://ojs.aaai.org/index.php/AAAI/article/view/5371). - `STAGN`: Graph Neural Network for Fraud Detection via Spatial-temporal Attention, published in [TKDE 2020](https://ieeexplore.ieee.org/abstract/document/9204584/) - `GTAN`: Semi-supervised Credit Card Fraud Detection via Attribute-driven Graph Representation, published in [AAAI 2023](https://ojs.aaai.org/index.php/AAAI/article/view/26702). - `RGTAN`: Enhancing Attribute-driven Fraud Detection with Risk-aware Graph Representation, published in [TKDE 2025](https://ieeexplore.ieee.org/document/10896835) - `HOGRL`: Effective High-order Graph Representation Learning for Credit Card Fraud Detection, published in [IJCAI 2024](https://www.ijcai.org/proceedings/2024/0839.pdf). ## Usage ### Data processing 1. Run `unzip /data/Amazon.zip` and `unzip /data/YelpChi.zip` to unzip the datasets; 2. Run `python feature_engineering/data_process.py` to pre-process all datasets needed in this repo. 3. Run `python feature_engineering/get_matrix.py` to generate the adjacency matrix of the high-order transaction graph.Please note that this will require approximately 280GB of storage space. Please be aware that if you intend to run `HOGRL` , you should first execute the `get_matrix.py` script. ### Training & Evalutaion To test implementations of `MCNN`, `STAN` and `STAGN`, run ``` python main.py --method mcnn python main.py --method stan python main.py --method stagn ``` Configuration files can be found in `config/mcnn_cfg.yaml`, `config/stan_cfg.yaml` and `config/stagn_cfg.yaml`, respectively. Models in `GTAN` and `RGTAN` can be run via: ``` python main.py --method gtan python main.py --method rgtan ``` For specification of hyperparameters, please refer to `config/gtan_cfg.yaml` and `config/rgtan_cfg.yaml`. Model in `HOGRL` can be run via: ``` python main.py --method hogrl ``` For specification of hyperparameters, please refer to `config/hogrl_cfg.yaml`. ### Data Description There are three datasets, YelpChi, Amazon and S-FFSD, utilized for model experiments in this repository. YelpChi and Amazon datasets are from [CARE-GNN](https://dl.acm.org/doi/abs/10.1145/3340531.3411903), whose original source data can be found in [this repository](https://github.com/YingtongDou/CARE-GNN/tree/master/data). S-FFSD is a simulated & small version of finacial fraud semi-supervised dataset. Description of S-FFSD are listed as follows: |Name|Type|Range|Note| |--|--|--|--| |Time|np.int32|from $\mathbf{0}$ to $\mathbf{N}$|$\mathbf{N}$ denotes the number of trasactions. | |Source|string|from $\mathbf{S_0}$ to $\mathbf{S}_{ns}$|$ns$ denotes the number of transaction senders.| |Target|string|from $\mathbf{T_0}$ to $\mathbf{T}_{nt}$ | $nt$ denotes the number of transaction reveicers.| |Amount|np.float32|from **0.00** to **np.inf**|The amount of each transaction. | |Location|string|from $\mathbf{L_0}$ to $\mathbf{L}_{nl}$ |$nl$ denotes the number of transacation locations.| |Type|string|from $\mathbf{TP_0}$ to $\mathbf{TP}_{np}$|$np$ denotes the number of different transaction types. | |Labels|np.int32|from **0** to **2**|**2** denotes **unlabeled**|| > We are looking for interesting public datasets! If you have any suggestions, please let us know! ## Test Result The performance of five models tested on three datasets are listed as follows: | |YelpChi| | |Amazon| | |S-FFSD| | | |:----|:----|:----|:----|:----|:----|:----|:----|:----|:----| | |AUC|F1|AP|AUC|F1|AP|AUC|F1|AP| |MCNN||- | -| -| -| -|0.7129|0.6861|0.3309| |STAN|- |- | -| -| -| -|0.7446|0.6791|0.3395| |STAGN|- |- | -| -| -| -|0.7659|0.6852|0.3599| |GTAN|0.9241|0.7988|0.7513|0.9630|0.9213|0.8838|0.8286|0.7336|0.6585| |RGTAN|0.9498|0.8492|0.8241|0.9750|0.9200|0.8926|0.8461|0.7513|0.6939| |HOGRL|0.9808|0.8595|-|0.9800|0.9198|-|-|-|-| > `MCNN`, `STAN` and `STAGN` are presently not applicable to YelpChi and Amazon datasets. > > `HOGRL` is presently not applicable to S-FFSD dataset. ## Repo Structure The repository is organized as follows: - `models/`: the pre-trained models for each method. The readers could either train the models by themselves or directly use our pre-trained models; - `data/`: dataset files; - `config/`: configuration files for different models; - `feature_engineering/`: data processing; - `methods/`: implementations of models; - `main.py`: organize all models; - `requirements.txt`: package dependencies; ## Requirements ``` python 3.7 scikit-learn 1.0.2 pandas 1.3.5 numpy 1.21.6 networkx 2.6.3 scipy 1.7.3 torch 1.12.1+cu113 dgl-cu113 0.8.1 ``` ### Contributors : ### Citing If you find *Antifraud* is useful for your research, please consider citing the following papers: @inproceedings{zou2024effective, title={Effective High-order Graph Representation Learning for Credit Card Fraud Detection.}, author={Zou, Yao and Cheng, Dawei}, booktitle={International Joint Conference on Artificial Intelligence}, year={2024} } @ARTICLE{xiang2025enhancing, author={Xiang, Sheng and Zhang, Guibin and Cheng, Dawei and Zhang, Ying}, journal={IEEE Transactions on Knowledge and Data Engineering}, title={Enhancing Attribute-Driven Fraud Detection With Risk-Aware Graph Representation}, year={2025}, pages={1-12}, doi={10.1109/TKDE.2025.3543887} } @inproceedings{Xiang2023SemiSupervisedCC, title={Semi-supervised Credit Card Fraud Detection via Attribute-driven Graph Representation}, author={Sheng Xiang and Mingzhi Zhu and Dawei Cheng and Enxia Li and Ruihui Zhao and Yi Ouyang and Ling Chen and Yefeng Zheng}, booktitle={Proceedings of the AAAI Conference on Artificial Intelligence}, year={2023} } @article{cheng2020graph, title={Graph Neural Network for Fraud Detection via Spatial-temporal Attention}, author={Cheng, Dawei and Wang, Xiaoyang and Zhang, Ying and Zhang, Liqing}, journal={IEEE Transactions on Knowledge and Data Engineering}, year={2020}, publisher={IEEE} } @inproceedings{cheng2020spatio, title={Spatio-temporal attention-based neural network for credit card fraud detection}, author={Cheng, Dawei and Xiang, Sheng and Shang, Chencheng and Zhang, Yiyi and Yang, Fangzhou and Zhang, Liqing}, booktitle={Proceedings of the AAAI Conference on Artificial Intelligence}, volume={34}, number={01}, pages={362--369}, year={2020} } @inproceedings{fu2016credit, title={Credit card fraud detection using convolutional neural networks}, author={Fu, Kang and Cheng, Dawei and Tu, Yi and Zhang, Liqing}, booktitle={International Conference on Neural Information Processing}, pages={483--490}, year={2016}, organization={Springer} }