Improving Attention Mechanism in Graph Neural Networks via Cardinality Preservation (IJCAI-2020)

Code for the Cardinality Preserved Attention (CPA) model proposed in our paper.

Requirements

CUDA 10.2
Python 3.6.9
Pytorch 1.6.0
Pytorch Geometric 1.6.1
Pytorch Scatter 2.0.5
Pytorch Sparse 0.6.7
NumPy
scikit-learn

When you have an environment with Python 3.6.9 and CUDA 10.2, the other dependencies can be installed with:

pip install -r requirements.txt

How to run

Unzip the data file:

unzip data.zip

Train and test our model:

python main.py

Optional arguments:

  --dataset         name of dataset
  --mod             model to be used: origin, additive, scaled, f-additive, f-scaled
  --seed            random seed
  --epochs          number of epochs to train
  --lr              initial learning rate
  --wd              weight decay value
  --n_layer         number of hidden layers
  --hid             size of input hidden units
  --heads           number of attention heads
  --dropout         dropout rate
  --alpha           alpha for the leaky_relu
  --kfold           number of kfold
  --batch_size      batch size
  --readout         readout function: add, mean

In our paper, the MUTAG, PROTEINS, ENZYMES, NCI1, REDDIT-BINARY and REDDIT-MULTI-5K datasets on https://chrsmrrs.github.io/datasets/docs/datasets/ are used. The other datasets listed on the website can also be used by directly changing the name of the dataset in ‘—dataset’. When you run the code, the needed dataset will be automatically downloaded and processed.

Cite

If you found this model and code are useful, please cite our paper:

@inproceedings{ijcai2020-194,
  title     = {Improving Attention Mechanism in Graph Neural Networks via Cardinality Preservation},
  author    = {Zhang, Shuo and Xie, Lei},
  booktitle = {Proceedings of the Twenty-Ninth International Joint Conference on
               Artificial Intelligence, {IJCAI-20}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},             
  editor    = {Christian Bessiere},    
  pages     = {1395--1402},
  year      = {2020},
  month     = {7},
  note      = {Main track}
  doi       = {10.24963/ijcai.2020/194},
  url       = {https://doi.org/10.24963/ijcai.2020/194},
}