atwoodduan/dga-gnn

The official implementation of the DGA-GNN algorithm.

DGA-GNN: Dynamic Grouping Aggregation GNN for Fraud Detection

This is the official implementation of the following paper:

DGA-GNN: Dynamic Grouping Aggregation GNN for Fraud Detection

Mingjiang Duan, Tongya Zheng, Yang Gao, Gang Wang, Zunlei Feng, Xinyu Wang

AAAI 2024 Main Track

Abstract

Fraud detection has increasingly become a prominent research field due to the dramatically increased incidents of fraud. The complex connections involving thousands, or even millions of nodes, present challenges for fraud detection tasks. Many researchers have developed various graph-based methods to detect fraud from these intricate graphs. However, those methods neglect two distinct characteristics of the fraud graph: the non-additivity of certain attributes and the distinguishability of grouped messages from neighbor nodes. This paper introduces the Dynamic Grouping Aggregation Graph neural network (DGA-GNN) for fraud detection, which addresses these two characteristics by dynamically grouping attribute value ranges and neighbor nodes. In DGA-GNN, we initially propose the decision tree binning encoding to transform non-additive node attributes into bin vectors. This approach aligns well with the GNN’s aggregation operation and avoids nonsensical feature generation. Furthermore, we devise a feedback dynamic grouping strategy to classify graph nodes into two distinct groups and then employ a hierarchical aggregation. This method extracts more discriminative features for fraud detection tasks. Extensive experiments on five datasets suggest that our proposed method achieves a 3%~16% improvement over existing SOTA methods.

Framework

Getting Started

1. Dataset Preparation:

• Download the dataset from this link.
• Place the downloaded fraud_graph_rawdata.7z file in the data directory.
• Decompress the dataset by executing:

  7z x fraud_graph_rawdata.7z

2. Data Preprocessing:

• Change the directory to the code folder:

  cd code

• Start the data preprocessing by running:

  python data_handle.py  

3. Training Models:

• run python train.py --config-name elliptic_of_amnet for Elliptic dataset
• run python train.py --config-name tfinancet for T-Finance dataset
• run python train.py --config-name tsocialt for T-Social dataset
• run python train.py --config-name yelpchit for YelpChi dataset
• run python train.py --config-name amazont for Amazon dataset

If you are familiar with wandb, you can set nowandb=False in the config.

Mainly Dependencies:

• torch==1.13.1
• dgl==1.1.2
• toad==0.1.1
• pandas==1.3.5
• numpy==1.21.5
• scikit-learn==1.0.2
• pytorch-lightning==1.9.4
• wandb==0.13.10
• hydra-core==1.3.2

Citation

If you use this package and find it useful, please cite our paper using the following BibTeX. Thanks! :)

@inproceedings{duan2024dgagnn,
  title={DGA-GNN: Dynamic Grouping Aggregation GNN for Fraud Detection},
  author={Duan, Mingjiang and Zheng, Tongya and Gao, Yang and Feng, Zunlei and Wang, Xinyu},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  year={2024}
}