On the Equivalence between Positional Node Embeddings and Structural Graph Representations

Overview:

This is the code for On the Equivalence between Positional Node Embeddings and Structural Graph Representations.

We evaluate different models on the node classification, link prediction and triad prediction tasks

Please see the supplementary section for a brief description and summary of the code.

Requirements

• PyTorch 1.2.0 or later - which can be downloaded here
• Python 3.6.1
• Pytorch-geometric - which can be downloaded here

We recommend training these models on a GPU.

Cora and Pubmed are described in Sen et al., 2008, and PPI in Zitnik and Leskovec, 2017. They are described in Hamilton 2017. Please see our paper for further details, including our test/train/validation splits.

Questions

Please feel free to reach out to Balasubramaniam Srinivasan (bsriniv at purdue.edu) if you have any questions.

Citation

If you use this code, please consider citing:

@inproceedings{
Srinivasan2020On,
title={On the Equivalence between Positional Node Embeddings and Structural Graph Representations},
author={Balasubramaniam Srinivasan and Bruno Ribeiro},
booktitle={International Conference on Learning Representations},
year={2020},
url={https://openreview.net/forum?id=SJxzFySKwH}
}