OpenWGL: Open-World Graph Learning

This repository contains the author's implementation Tensorflow in for our ICDM 2020 paper "OpenWGL: Open-World Graph Learning".

Dependencies

• Python (>=3.6)
• Tensorflow/Tensorflow-gpu: (>= 1.14.0 or >= 2.0.0b1)
• numpy (>=1.17.4)
• scipy (>= 1.1.0)
• tf_geometric (>=1.0)

Datasets

The data folder can contain many graph datasets, and we provide the default Cora dataset.

Implementation

Here we provide the implementation of OpenWGL, along with the default dataset (Cora). The repository is organised as follows:

• data/ contains the necessary dataset files (more datasets can be found in tf_geometric);
• opgl/ contains the implementation of the VGAE and the basic utils;

Finally, OpenWGL_demo.py puts all of the above together and can be used to execute a full training run on the datasets.

Process

• Place the datasets in data/
• Change the dataset in OpenWGL_demo.py .
• Training/Testing:

python OpenWGL_demo.py

• Or

 python OpenWGL_demo.py --dataset_name cora --unseen_num 1

Citation

@inproceedings{wu2020OPGL
author={Man Wu and Shirui Pan and Xingquan Zhu},
title={OpenWGL: Open-World Graph Learning},
booktitle={20th {IEEE} International Conference on Data Mining, ICDM},
year={2020}
}

Some of the code was forked from the following repository

tf_geometric