Transformer over Directed Acyclic Graph (NeurIPS 2023)

The repository implements the Transformer over Directed Acyclic Graph (DAG transformer) in Pytorch Geometric.

Installation

Tested with Python 3.7, PyTorch 1.13.1, and PyTorch Geometric 2.3.1.

The dependencies are managed by [conda]:

pip install -r requirements.txt

Overview

• ./NA Experiment code over the NA dataset.

• ./ogbg-code2 Experiment code over the ogbg-code2 data from OGB.

• ./self-citation Experiment code over the self-citation dataset.

• ./Node_classification_citation Experiment code over the Cora, Citeseer, Pubmed datasets.

Reference

If you find our codes useful, please consider citing our work

@inproceedings{
luo2023transformers,
title={Transformers over Directed Acyclic Graphs},
author={Yuankai Luo and Veronika Thost and Lei Shi},
booktitle={Thirty-seventh Conference on Neural Information Processing Systems},
year={2023},
url={https://openreview.net/forum?id=g49s1N5nmO}
}

Poster