Open Temporal Graph | OTGNet |
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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.
Clone the repo and build the environment
git clone https://github.com/tulerfeng/OTGNet.git
conda env create -f environment.yml
conda activate OTGNet
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
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
This implementation is based on code from several repositories.
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} }
All code within this repository is under Apache License 2.0.