This repo provides a reference implementation of EcoGNN as described in the paper:

"EcoGNN: An Information Cascade Prediction Framework" This paper will be released soon

The code was tested with python 3.7, pytorch 1.10, cudatoolkit 11.3.1, and cudnn 6.0. Install the dependencies via Anaconda:

# create virtual environment
conda create --name eocognn python=3.7 cudatoolkit=11.3.1 cudnn=6.0

# activate environment
conda activate ecognn

# install other requirements
pip install -r requirements.txt

cd ./EcoGNN

# generate information cascades
python ./preprocess/gene_cas.py 

# generate global graph embeddings
python ./preprocess/Global_embedding_processing_all.py 

# preprocess cascades data for training
python ./preprocess/preprocess_trainDataSet.py

# run EcoGNN model
python EcoGNN_train_shuffle.py

More running options are described in the codes.

In addition, we also provide already trained models for you to reproduce the experimental effects in the paper.

# run evaluate EcoGNN model
python EcoGNN_metric.py

Our's datasets from CasFlow.

Thanks to Xovee Xu for providing the dataset. 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: [email protected],[email protected]