# TrustRD

**Repository Path**: thinking024/TrustRD

## Basic Information

- **Project Name**: TrustRD
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2023-11-15
- **Last Updated**: 2023-11-15

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# TrustRD
Source code for paper **Towards Trustworthy Rumor Detection with Interpretable Graph
Structural Learning**

## Dependencies
python 3.7    
pytorch 1.8.1   
pytorch_geometric 1.7.0 


## Usage

```
python ./Process/getTwittergraph.py Twitter15 # Encode graph for Twitter15
python ./Process/getTwittergraph.py Twitter16 # Encode graph for Twitter16
python ./Model/train.py Twitter15 100 # Run TrustRD for 100 iterations on Twitter15 dataset
python ./Model/train.py Twitter16 100 # Run TrustRD for 100 iterations on Twitter16 dataset
```
In most cases, the best performance will be obatined during 20-30 epochs after beginning of fine-tuning process. If you feel time-consuming to run the pre-process, please directly load our pre-process model, which is given in the folder.

## Dataset
We use Twitter15 and Twitter16 dataset for the experiment.    
To learn more about the dataset, please refer to [RvNN](https://github.com/majingCUHK/Rumor_RvNN) for more details.