This repo provides a reference implementation of CE-GCN as described in the paper:
Cascade-Enhanced Graph Convolutional Network for Information Diffusion Prediction
You can find the dataset in the "data" folder, which contains all three datasets (Twitter, Douban, and Meme).
Our experiments are conducted on CentOS 20.04, a single NVIDIA V100 GPU. CCGL is implemented by Python 3.7, Torch 1.0.9.
Create a virtual environment and install GPU-support packages via Anaconda:
# create virtual environment
conda create --name=CEGCN python=3.9
# activate virtual environment
conda activate CEGCN
# install other related dependencies
conda install wandb
conda install pytorch==1.8.0 torchvision==0.9.0 torchaudio==0.8.0 cudatoolkit=10.2 -c pytorch
conda install pyg -c pyg -c conda-forge
conda install scikit-learn-intelexYou can run our model with the following commands:
CUDA_VISIBLE_DEVICES=1 python run.py --data="twitter"
CUDA_VISIBLE_DEVICES=1 python run.py --data="douban"
CUDA_VISIBLE_DEVICES=1 python run.py --data="memetracker"
CUDA_VISIBLE_DEVICES=1 nohup python run.py --data="twitter" &
CUDA_VISIBLE_DEVICES=0 nohup python run.py --data="douban" &
CUDA_VISIBLE_DEVICES=1 nohup python run.py --data="memetrackerCUDA_VISIBLE_DEVICES=1 nohup python run.py --data="twitter" --notes="item" &
CUDA_VISIBLE_DEVICES=0 nohup python run.py --data="twitter" --notes="social" &
CUDA_VISIBLE_DEVICES=0 nohup python run.py --data="twitter" --notes="diffusion" &
CUDA_VISIBLE_DEVICES=1 nohup python run.py --data="twitter" --notes="social+item" &
CUDA_VISIBLE_DEVICES=1 nohup python run.py --data="twitter" --notes="diffusion+item" &
CUDA_VISIBLE_DEVICES=1 nohup python run.py --data="twitter" --notes="social+diffusion" &
CUDA_VISIBLE_DEVICES=1 nohup python run.py --data="douban" --notes="item" &
CUDA_VISIBLE_DEVICES=0 nohup python run.py --data="douban" --notes="social" &
CUDA_VISIBLE_DEVICES=1 nohup python run.py --data="douban" --notes="diffusion" &
CUDA_VISIBLE_DEVICES=0 nohup python run.py --data="douban" --notes="social+item" &
CUDA_VISIBLE_DEVICES=1 nohup python run.py --data="douban" --notes="diffusion+item" &
CUDA_VISIBLE_DEVICES=1 nohup python run.py --data="douban" --notes="social+diffusion" &If you find our paper & code are useful for your research, please consider citing us 😘:
@inproceedings{DBLP:conf/dasfaa/WangWYBZZH22,
author = {Ding Wang and
Lingwei Wei and
Chunyuan Yuan and
Yinan Bao and
Wei Zhou and
Xian Zhu and
Songlin Hu},
editor = {Arnab Bhattacharya and
Janice Lee and
Mong Li and
Divyakant Agrawal and
P. Krishna Reddy and
Mukesh K. Mohania and
Anirban Mondal and
Vikram Goyal and
Rage Uday Kiran},
title = {Cascade-Enhanced Graph Convolutional Network for Information Diffusion
Prediction},
booktitle = {Database Systems for Advanced Applications - 27th International Conference,
{DASFAA} 2022, Virtual Event, April 11-14, 2022, Proceedings, Part
{I}},
series = {Lecture Notes in Computer Science},
volume = {13245},
pages = {615--631},
publisher = {Springer},
year = {2022},
url = {https://doi.org/10.1007/978-3-031-00123-9\_50},
doi = {10.1007/978-3-031-00123-9\_50},
timestamp = {Fri, 29 Apr 2022 14:50:40 +0200},
biburl = {https://dblp.org/rec/conf/dasfaa/WangWYBZZH22.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}For any questions please open an issue or drop an email to: [email protected]
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