We provide the code in the code in the code folder and the COVID-19 Vaccine Tweet data (including sequences and well contructed graph) in the data folder.

In the code/run_cmd folder we provide the bash file for training of VigDet and the pre-training code (based on AMDN-HAGE). The hyperparameters are also included in the bash files.
In each bash file of pre-trainng, you will need to set the directory storing the data and the $out_dir where the well-trained model and embedding will be stored.
In each bash file of training (starting with 'run_'), you will additionally need a graph file path to the constructed graph.
To train a VigDet, you will need to first run the pre-training code to acquire the pre-trained model. After that, put the $best_gmm.pt and $best_model_state_dict.pt in the $out_dir of VigDet. Then run the training bash file. It will automatically read the two pre-trained model and start training.

For IRA dataset experiment, we did not put the data in because we have no permission to distribute the data.
If you want to reproducing the experiments on IRA dataset, please see our checklist for the contact information of the data.

We run the experiments of COVID-19 vaccine tweets dataset on a server with 4 GPUs. The current code only supports 4 GPUs. To change the GPU number, you need to change the code in the BalancedDataParallel class and the sum_vec. We will provide a more flexible implementation that supports different number of GPUs if the paper gets accpeted.