OTGNet: Towards Open Temporal Graph Neural Networks (ICLR 2023)

Open Temporal Graph	OTGNet

Introduction

This repository contains the code for paper: Towards Open Temporal Graph Neural Netwroks. This paper is accepted as ICLR 2023 notable-top-5% paper.

TL;DR: We propose a general and principled learning approach for open temporal graphs where the class set for nodes is open.

Setup

Clone the repo and build the environment

git clone https://github.com/tulerfeng/OTGNet.git
conda env create -f environment.yml
conda activate OTGNet

Dataset

Download raw data of three datasets and use the code in ./process_raw_data folder to process the raw data with GloVe

We also provide the processed datasets for direct use which could be downloaded at here.

Put the processed datasets in ./data folder

Training

Train model on the Reddit dataset

python run.py reddit

Train model on the Yelp dataset

python run.py yelp

Train model on the Taobao dataset

python run.py taobao

Acknowledgement

This implementation is based on code from several repositories.

Citation

If you find our repo, dataset or paper useful, please cite us as

@inproceedings{fengtowards,
  title={Towards Open Temporal Graph Neural Networks},
  author={Feng, Kaituo and Li, Changsheng and Zhang, Xiaolu and ZHOU, JUN},
  booktitle={International Conference on Learning Representations}
}

License

All code within this repository is under Apache License 2.0.