Graph Neural Networks with
Learnable Structural and Positional Representations

Source code for the paper "Graph Neural Networks with Learnable Structural and Positional Representations" by Vijay Prakash Dwivedi, Anh Tuan Luu, Thomas Laurent, Yoshua Bengio and Xavier Bresson, at the Tenth International Conference on Learning Representations (ICLR) 2022.

We propose a novel GNN architecture in which the structural and positional representations are decoupled, and are learnt separately to learn these two essential properties. The architecture, named MPGNNs-LSPE (MPGNNs with Learnable Structural and Positional Encodings), is generic that it can be applied to any GNN model of interest which fits into the popular 'message-passing framework', including Transformers.

1. Repo installation

Follow these instructions to install the repo and setup the environment.

2. Download datasets

Proceed as follows to download the benchmark datasets.

3. Reproducibility

Use this page to run the codes and reproduce the published results.

4. Reference

:page_with_curl: Paper on arXiv
:movie_camera: Video by @vijaydwivedi75 on YouTube
:movie_camera: Video by @xbresson on YouTube

@inproceedings{dwivedi2022graph,
  title={Graph Neural Networks with Learnable Structural and Positional Representations},
  author={Vijay Prakash Dwivedi and Anh Tuan Luu and Thomas Laurent and Yoshua Bengio and Xavier Bresson},
  booktitle={International Conference on Learning Representations},
  year={2022},
  url={https://openreview.net/forum?id=wTTjnvGphYj}
}