Code for the AAAI 2022 paper "DDGCN: Dual Dynamic Graph Convolutional Networks for Rumor Detection on Social Media"
Make sure the following files are present as per the directory structure before running the code,
DDGCN
├── README.md
├── preprocess
| ├── *.py
| └── *.txt
├── models
| ├── *.py
| └── model_saved
| └── ckpt_nn.model
├── data
| └── pheme
| ├── all-rnr-annotated-threads
| │ ├── ebola-essien-all-rnr-threads
| │ ├── charliehebdo-all-rnr-threads
| ├── ......
| │ └── sydneysiege-all-rnr-threads
| ├── pheme_clean
| ├── pheme_concept_yago
| ├── pheme_entity
| ├── pheme_temporal_data
| ├── mid2text.txt
| ├── mid2token.csv
| ├── node2idx.txt
| ├── node2idx_mid.txt
| ├── node2idx_test.txt
| └── pheme_id_label.txt
└── requirement.txt
pheme_clean, pheme_concept_yago, pheme_entity and pheme_temporal_data these four folders are packed into a zip file, and can be obtained from https://www.dropbox.com/s/xwn5dvqgx2n2vsd/pheme_peocessed_data.zip?dl=0. The Raw Pheme dataset can be obtained from https://figshare.com/articles/dataset/PHEME_dataset_for_Rumour_Detection_and_Veracity_Classification/6392078 (or https://www.dropbox.com/s/j8x105s60ow997f/all-rnr-annotated-threads.zip?dl=0).
The raw Weibo dataset can be downloaded from https://www.dropbox.com/s/46r50ctrfa0ur1o/rumdect.zip?dl=0 . More information about this dataset is available here 'https://github.com/majingCUHK/Rumor_GAN'.
Our code runs with the following packages installed:
python 3.6
torch 1.4.0+cu100
torch-cluster 1.5.2
torch-geometric 1.7.2
torch-scatter 2.0.3
torch-sparse 0.5.1
tqdm
numpy
pandas
matplotlib
scikit_learn
install the virtual environment with
pip install -r requirement.txt
Train and test,
python train_dynamic.py --dataset pheme --model completed --cuda 1 --batch 32 --epoch 5 --lr 0.001