This repo provides a reference implementation of Contrastive Cascade Graph Learning (CCGL) framework as described in the paper:

CCGL: Contrastive Cascade Graph Learning
Xovee Xu, Fan Zhou, Kunpeng Zhang, and Siyuan Liu
IEEE Transactions on Knowledge and Data Engineering (TKDE), 2022
arXiv:2107.12576

You can download all five datasets (Weibo, Twitter, ACM, APS, and DBLP) via any one of the following links:

Google Drive	Dropbox	Onedrive	Baidu Netdisk
			trqg

Our experiments are conducted on CentOS Linux release 7.8.2003, a single NVLink A100 40GB GPU. CCGL is implemented by Python 3.9, pytorch 1.11, Cuda 11.4.

Create a virtual environment and install GPU-support packages via Anaconda:

# create virtual environment
conda create --name=ccgl python=3.9 cudatoolkit=11.4

# activate virtual environment
conda activate ccgl

# install other dependencies
pip install -r requirements.txt

Here we take Weibo dataset as an example to demonstrate the usage.

Step 1: divide, filter, generate labeled and unlabeled cascades:

cd ccgl
# labeled cascades
python src/gene_cas.py --input=./datasets/weibo/ --unlabel=False
# unlabeled cascades
python src/gene_cas.py --input=./datasets/weibo/ --unlabel=True

Step 2: augment both labeled and unlabeled cascades (here we use the AugSIM strategy):

python src/augmentor.py --input=./datasets/weibo/ --aug_strategy=AugSIM

Step 3: generate cascade embeddings:

python src/gene_emb.py --input=./datasets/weibo/ 

python src/pre_training.py --name=weibo-0 --input=./datasets/weibo/ --projection_head=4-1

The saved pre-training model is named as weibo-0.

python src/fine_tuning.py --name=weibo-0 --num=0 --input=./datasets/weibo/ --projection_head=4-1

Here we load the pre-trained model weibo-0 and save the teacher network as weibo-0-0.

python src/distilling.py --name=weibo-0-0 --num=0 --input=./datasets/weibo/ --projection_head=4-1

Here we load the teacher network weibo-0-0 and save the student network as weibo-0-0-student-0.

Unless otherwise specified, we use following default hyper-parameter settings.

If you find our paper & code are useful for your research, please consider citing us 😘:

@article{xu2022ccgl, 
  author = {Xovee Xu and Fan Zhou and Kunpeng Zhang and Siyuan Liu}, 
  title = {{CCGL}: Contrastive Cascade Graph Learning}, 
  journal = {IEEE Transactions on Knowledge and Data Engineering (TKDE)},
  numpages = {15},
  year = {2022}, 
}

We also have a survey paper you might be interested:

@article{zhou2021survey,
  author = {Fan Zhou and Xovee Xu and Goce Trajcevski and Kunpeng Zhang}, 
  title = {A Survey of Information Cascade Analysis: Models, Predictions, and Recent Advances}, 
  journal = {ACM Computing Surveys (CSUR)}, 
  volume = {54},
  number = {2},
  year = {2021},
  articleno = {27},
  numpages = {36},
  doi = {10.1145/3433000},
}