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), vol. 35, no. 5, pp. 4539--4554, May 2023
arXiv:2107.12576
https://doi.org/10.1109/TKDE.2022.3151829

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 Ubuntu 20.04, a single NVIDIA 1080Ti GPU, 48GB RAM, and Intel i7 8700K. CCGL is implemented by Python 3.7, TensorFlow 2.3, Cuda 10.1, and Cudnn 7.6.5.

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

# create virtual environment
conda create --name=ccgl python=3.7 cudatoolkit=10.1 cudnn=7.6.5

# 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.

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

We provide pre-trained, fine-tuned, and distilled CCGL model weights. Please see details in the following table.

Load weights into the model:

# construct model, carefully check projection head designs:
# use different number of Dense layers
...
# load weights for fine-tuning, distillation, or evaluation
model.load_weights(weight_path)

Check src/fine_tuning.py and src/distilling.py for weights loading examples.

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

• Jul 21, 2021: fix a bug and some annotations

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)},
  volume = {35},
  number = {5},
  pages = {4539--4554},
  year = {2022},
  doi = {10.1109/TKDE.2022.3151829}, 
}

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},
}

We would like to thank Xiuxiu Qi, Ce Li, Qing Yang, and Wenxiong Li for sharing their computing resources and help us to test the codes. We would also like to show our gratitude to the authors of SimCLR (and Sayak Paul), node2vec, DeepHawkes, and others, for sharing their codes and datasets.

For any questions please open an issue or drop an email to: xovee at live.com