This repository is an implementation of our proposed CoupledGNN model in the following paper:
Qi Cao, Huawei Shen, Jinhua Gao, Bingzheng Wei, Xueqi Cheng. 2020. Popularity Prediction on Social Platforms
with Coupled Graph Neural Networks. In WSDM'20, February 3-7, 2020, Houston, TX, USA, 9 pages.
The CoupledGNN model solves the network-aware popularity prediction problem, capturing the cascading effect explicitly by two coupled graph neural networks.
For more details, you can download this paper Here
Python 2.7.5
Tensorflow 1.14.0
Example Usage
python -u train.py --dataset=artificial1 --learning_rate=5e-4 --graph_learning_rate=5e-5 --n_layers=3
For detailed description of all parameters, you can run
python -u train.py --help
Please cite our paper if you use this code in your own work:
@inproceedings{cao2020coupledgnn,
title={Popularity Prediction on Social Platforms with Coupled Graph Neural Networks},
author={Cao, Qi and Shen, Huawei and Gao, Jinhua and Wei, Bingzheng and Cheng, Xueqi},
booktitle={Proceedings of the 13th ACM International Conference on Web Search and Data Mining},
series={WSDM'20},
year={2020},
location={Houston, TX, USA},
numpages={9}
}