CapsGNN

This repository contains an official TensorFlow implementation of Capsule Graph Neural Network (CapsGNN).

The implementation of dynamic routing refers to the [code]

Package Version

networkx    2.2
numpy       1.16.2
scipy       1.2.1
argparse    1.1
tensorflow  1.12.1

Basic Usage

Data Preparation

1. We provide the preprocessing program to generate specific experimental data format. The default raw data format should be .gexf (avalaible at [gexf Dataset]). Each line of the label file represents a graph with the format
   

    xxx.gexf label

To generate experimental data format:

    $ python3 dataset_utils/preprocessing.py --dataset_input_dir graph_gexf/ENZYMES --output_data_dir data_plk --pickle_v 3 --x_fold 10 --gen_split_file True

Execute

1. All the hyperparameters can be set in config.py and the training procedure can be executed through:

    $ python3 main.py --dataset_dir data_plk/ENZYMES --epochs 3000 --lambda_val 0.5

Citing

If you find CapsGNN is useful for your research, please consider citing the following paper:

@inproceedings{xinyi2018capsule,
   title={Capsule Graph Neural Network},
   author={Zhang Xinyi and Lihui Chen},
   booktitle={International Conference on Learning Representations},
   year={2019},
   url={https://openreview.net/forum?id=Byl8BnRcYm},
  }

Please send any questions you might have about the codes and/or the algorithm to xinyi001@e.ntu.edu.sg.