Paper: Information Cascade Popularity Prediction via Probabilistic Diffusion
The code was tested with python 3.7, pyorch-gpu 1.10, cudatookkit 10.2, and cudnn 7.6.5. Install the dependencies via Anaconda:
# create virtual environment
conda create --name CasDO python=3.7 cudatoolkit=10.2 cudnn=7.6.5
# activate environment
conda activate CasDO
# install other requirements
pip install -r requirements.txtcd ./CasDO
# generate information cascades
python gene_cas.py --input=./dataset/twitter/
# generate cascade graph and global graph embeddings
python gene_emb.py --input=./dataset/twitter/
# run CasDO model
python run_model.py --input=./dataset/twitter/More running options are described in the codes, e.g., --input=./dataset/twitter/
Datasets download link: Google Drive or Baidu Drive (password: 1msd).
The datasets we used in the paper are come from:
- Twitter (Weng et al., Virality Prediction and Community Structure in Social Network, Scientific Report, 2013).
- Weibo (Cao et al., DeepHawkes: Bridging the Gap between Prediction and Understanding of Information Cascades, CIKM, 2017). You can also download Weibo dataset here in Google Drive.
- APS (Released by American Physical Society, obtained at Jan 17, 2019).
For any questions please open an issue or drop an email to: zhangtao980107@outlook.com