Source code for GNN-LSPE (Graph Neural Networks with Learnable Structural and Positional Representations)

Related tags

Deep Learning graphs transformers attention representation-learning molecules lspe geometric-deep-learning graph-neural-networks graph-representation-learning gnn graph-deep-learning positional-encoding graph-transformer transformer-networks gnn-lspe

Overview

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.

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

📃 Paper on arXiv

@article{dwivedi2021graph,
  title={Graph Neural Networks with Learnable Structural and Positional Representations},
  author={Dwivedi, Vijay Prakash and Luu, Anh Tuan and Laurent, Thomas and Bengio, Yoshua and Bresson, Xavier},
  journal={arXiv preprint arXiv:2110.07875},
  year={2021}
}

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Comments

Avoid h being overwritten by residual p

Hi,

Congratulations for the nice paper&repo!

I think I found a small typo that could influence the performance of running PNA with residual=True, see attached.

Best, Andreea

opened by andreeadeac22 3
Migrate to partial node classification tasks

Dear Author,

Thanks so much for the code!

I don't know if LSPE can be used for sub-graph node classification (prediction on part of the nodes). I checked another repo you wrote (https://github.com/graphdeeplearning/benchmarking-gnns) and it seems I have to generate the features of all the nodes/edges first.

opened by ChengFu0118 1

Source code for GNN-LSPE (Graph Neural Networks with Learnable Structural and Positional Representations)

Related tags

Overview

Graph Neural Networks with
Learnable Structural and Positional Representations

1. Repo installation

2. Download datasets

3. Reproducibility

4. Reference

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Owner

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