VPGNN: Voucher Abuse Detection with Prompt-based Fine-tuning on Graph Neural Networks

We provide the implementaion of VPGNN model, which is the source code for CIKM 2023 paper "Voucher Abuse Detection with Prompt-based Fine-tuning on Graph Neural Networks".

The repository is organised as follows:

• data/: the directory of data sets, and it contains the Amazon data set as the example.
• res/: the directory of saved models.
• data_process.py: data preperation for node features, lables, edges.
• pre_train.py: DGI pre-training
• prompt.py: make well initialized prompt be learnable vectors.
• final_model_gnn.py: integrate the prompt and gnn into the final model.
• main.py: prompt-based fine-tuning and final prediction.

Requirements

To install requirements:

pip install -r requirements.txt

Data

In VPGNN directory, to unzip the datasets, run:

unzip /data/Amazon.zip

To generate node feature, label, edges, run:

python data_process.py

Train and test

To pre-train the model in the paper:

python pre_train.py

Prompt-based fine-tuning and final prediction:

python main.py